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With the advance of industrialisation and urbanisation, land use change and climate change have significant impacts on the global ecosystem. Focusing on Chongqing Municipality, a municipality that plays a central role in regional economic development and national strategies, this study explores the impacts of land use and climate change on the evolution of its NDVI spatial and temporal patterns between 2000 and 2020 and reveals the driving mechanisms behind them. By analysing remote sensing image data and climate data, it was found that Chongqing Municipality experienced significant land use changes during the study period, especially urban expansion and the reduction of agricultural land, which led to the reduction of vegetation cover. Meanwhile, precipitation in climate change positively affected vegetation growth and coverage, while the increase in surface temperature during urbanisation negatively affected vegetation cover and exacerbated the urban heat island effect. NDVI was positively correlated with precipitation and negatively correlated with air temperature, suggesting that moderate precipitation promotes vegetation growth, while high temperatures may adversely affect vegetation activities. The results of this study can provide a scientific basis for urban planning and ecological conservation, especially in formulating effective urban management and land management strategies to protect the ecological environment and rationally utilise land resources. [ABSTRACT FROM AUTHOR]

The Yellow River Basin serves as a critical ecological barrier in China. However, it has increasingly faced severe ecological and environmental challenges, with soil erosion and overgrazing being particularly prominent issues. As an important region in the middle and lower reaches of the Yellow River, the Jinan section of the Yellow River Basin is similarly affected by these problems, posing significant threats to the stability and sustainability of its ecosystems. To scientifically identify areas severely impacted by soil erosion and systematically quantify the effects of climate change on vegetation coverage within the Yellow River Basin, this study focuses on the Jinan section. By analyzing the spatio-temporal evolution patterns of the Normalized Difference Vegetation Index (NDVI), this research aims to explore the driving mechanisms behind these changes and further predict the future spatial distribution of NDVI, providing theoretical support and practical guidance for regional ecological conservation and sustainable development. This study employed the slope trend analysis method to examine the spatio-temporal variation characteristics of NDVI in the Jinan section of the Yellow River Basin from 2008 to 2022 and utilized the FLUS model to predict the spatial distribution of NDVI in 2025. The Optimal Parameters-based Geographical Detector (OPGD) model was applied to systematically analyze the impacts of four key driving factors—precipitation (PRE), temperature (TEM), population density (POP), and gross domestic product (GDP) on vegetation coverage. Finally, correlation and lag effect analyses were conducted to investigate the relationships between NDVI and TEM as well as NDVI and PRE. The research results indicate the following: (1) from 2008 to 2022, the NDVI values during the growing season in the Jinan section of the Yellow River Basin exhibited a significant increasing trend. This growth suggests a continuous improvement in regional vegetation coverage, likely influenced by the combined effects of natural and anthropogenic factors. (2) The FLUS model predicts that, by 2025, the proportion of high-density NDVI areas will rise to 55.35%, reflecting the potential for further optimization of vegetation coverage under appropriate management. (3) POP had a particularly significant impact on vegetation coverage, and its interaction with TEM, PRE, and GDP generated an amplified combined effect, indicating the dominant role of the synergy between socioeconomic and climatic factors in regional vegetation dynamics. (4) NDVI exhibited a significant positive correlation with both temperature and precipitation, further demonstrating that climatic conditions were key drivers of vegetation coverage changes. (5) In urban areas, NDVI showed a certain time lag in response to changes in precipitation and temperature, whereas this lag effect was not significant in suburban and mountainous areas, highlighting the regulatory role of human activities and land use patterns on vegetation dynamics in different regions. These findings not only reveal the driving mechanisms and influencing factors behind vegetation coverage changes but also provide critical data support for ecological protection and economic development planning in the Yellow River Basin, contributing to the coordinated advancement of ecological environment construction and economic growth. [ABSTRACT FROM AUTHOR]

The middle reaches of the Yellow River Basin (MYRB) are known for their significant soil erosion and fragile ecological environment, where vegetation growth is important. However, the vegetation's reaction to climate change (CC) and human activity (HA), and the potential driving mechanisms underlying such changes in the MYRB, have not yet been clarified. Thus, based on remote sensing data, combined with trend analysis and the Hurst method and supplemented by the structural equation model (SEM) and residual analysis method, we aimed to conduct an analysis of the spatio-temporal evolution of the normalized difference vegetation index (NDVI) in the MYRB from 2000 to 2020. Additionally, we explored how climate and human factors together affect the NDVI and quantified the proportion of their respective contributions to NDVI change. The NDVI exhibited a fluctuating upward trend in the MYRB. Moreover, approximately 97.7% of the area showed an improving trend, with nearly 50% of the area continuing to maintain an improving trend. Precipitation and temperature had positive effects on the NDVI, while vapor pressure deficit (VPD) and land use intensity (LUI) had negative effects. HA played a pivotal role in the vegetation improvement area with a contribution rate of 67.53%. The study revealed NDVI variations and emphasized the influence of HA on the NDVI in the MYRB. The findings are vital in comprehending the response mechanism of ecosystems and guiding reasonable environmental protection policies, which is beneficial for the sustainable development of the region. [ABSTRACT FROM AUTHOR]

Remote sensing has proven to be a vital tool for monitoring and forecasting the quality and yield of crops. The utilization of innovative technologies such as Solar-Induced Fluorescence (SIF) and satellite measurements of column-averaged CO2 (xCO2) can enhance these estimations. SIF is a signal emitted by crops during photosynthesis, thus indicating photosynthetic activities. The concentration of atmospheric CO2 is a critical factor in determining the efficiency of photosynthesis. The aim of this study was to investigate the correlation between satellite-derived Solar-Induced Chlorophyll Fluorescence (SIF), column-averaged CO2 (xCO2), and Normalized Difference Vegetation Index (NDVI) and their association with sugarcane yield and sugar content in the field. This study was carried out in south-central Brazil. We used four localities to represent the region: Pradópolis, Araraquara, Iracemápolis, and Quirinópolis. Data were collected from orbital systems during the period spanning from 2015 to 2016. Concurrently, monthly data regarding tons of sugarcane per hectare (TCH) and total recoverable sugars (TRS) were gathered from 24 harvest locations within the studied plots. It was observed that TRS decreased when SIF values ranged between 0.4 W m−2 sr−1 μm−1 and 0.8 W m−2 sr−1 μm−1, particularly in conjunction with NDVI values below 0.5. TRS values peaked at 15 kg t−1 with low NDVI and xCO2 values, alongside SIF values lower than 0.4 W m−2 sr−1 μm−1 and greater than 1 W m−2 sr−1 μm−1. These findings underscore the potential of integrating SIF, xCO2, and NDVI measurements in the monitoring and forecasting of yield and sugar content in sugarcane crops. [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]

Net Primary Productivity (NPP) is a crucial indicator of ecological environment quality. To better understand the carbon absorption and carbon cycling capabilities of Henan Province, this study investigates the trends and driving factors of NPP across different climatic zones and land use types. The Theil–Sen Median trend analysis method and the Mann–Kendall trend test are employed to monitor NPP changes from 2001 to 2020. The average annual NPP in Henan Province during this period was 414.61 gC·m−2·year−1, showing a significant increasing trend with a growth rate of 3.73 gC·m−2·year−1. Spatially, both the annual average NPP and its increase rate were higher in the western part of Henan compared to the eastern part, and NPP variability was more stable in the southern region than in the northern region. By classifying climatic zones and using the Geodetector method to assess NPP sensitivity to natural factors, the results show that climate and vegetation factors jointly influence NPP variations, with annual precipitation being the primary natural factor affecting NPP trends in Henan Province from 2001 to 2020. By analyzing the NPP gain and loss matrix, the impact of land use changes on NPP was evaluated. Forests had the highest average annual NPP at 483.52 gC·m−2·year−1, and the conversion of arable land to urban areas was identified as the primary land change type leading to NPP reductions. In the subtropical zone of Henan, forests, croplands, and grasslands exhibited higher NPP values and increase rates compared to those in the warm belt. This study provides new insights into the spatial variation of NPP caused by changes in climatic zones and land use types. [ABSTRACT FROM AUTHOR]

Agroecosystems are facing the adverse effects of climate change. This study explored how the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) can give new insight into irrigation allocation and plant health. Leveraging the global coverage and 70-m spatial resolution of the Evaporative Stress Index (ESI) from ECOSTRESS, we processed over 200 overpasses and examined patterns over 3 growing seasons across the Maipo River Basin of Central Chile, which faces exacerbated water stress. We found that ECOSTRESS ESI varies substantially based on the overpass time, with ESI values being systematically higher in the morning and lower in the afternoon. We also compared variations in ESI against spatial patterns in the environment. To that end, we analyzed the vegetation greenness sensed from Landsat 8 and compiled the referential irrigation allocation from Chilean water regulators. Consistently, we found stronger correlations between these variables and ESI in the morning time (than in the afternoon). Based on our findings, we discussed new insights and potential applications of ECOSTRESS ESI in support of improved agricultural monitoring and sustainable water management. [ABSTRACT FROM AUTHOR]

Remote sensing technology is widely used to obtain information on floating green tides, and thresholding methods based on indices such as the normalized difference vegetation index (NDVI) and the floating algae index (FAI) play an important role in such studies. However, as the methods are influenced by many factors, the threshold values vary greatly; in particular, the error of data extraction clearly increases in situations of high-turbidity water (HTW) (NDVI > 0). In this study, high spatial resolution, multispectral images from the Sentinel-2 MSI mission were used as the data source. It was found that the International Commission on Illumination (CIE) hue angle calculated using remotely sensed equivalent multispectral reflectance data and the RGB method is extremely effective in distinguishing floating green tides from areas of HTW. Statistical analysis of Sentinel-2 MSI images showed that the threshold value of the hue angle that can effectively eliminate the effect of HTW is 218.94°. A test demonstration of the method for identifying the floating green tide in HTW in a Sentinel-2 MSI image was carried out using the identified threshold values of NDVI > 0 and CIE hue angle < 218.94°. The demonstration showed that the method effectively eliminates misidentification caused by HTW pixels (NDVI > 0), resulting in better consistency of the identification of the floating green tide and its distribution in the true color image. The method enables rapid and accurate extraction of information on floating green tide in HTW, and offers a new solution for the monitoring and tracking of green tides in coastal areas. [ABSTRACT FROM AUTHOR]

Ecological protection in the Yellow River Basin (YRB) is a major strategy for China's sustainable development. Amid global warming, droughts have occurred more frequently, severely affecting vegetation growth. Based on the Standardized Precipitation Evapotranspiration Index (SPEI) and Normalized Difference Vegetation Index (NDVI) at different time scales from 2003 to 2020, this study employed the linear trend method and the Spearman correlation coefficient method to calculate the trends and correlation coefficients of NDVI and SPEI at different scales at the pixel scale and explored the spatial distribution pattern of the sensitivity of vegetation growth in the YRB to drought. The results show that: (1) NDVI and SPEI are positively correlated in 77% of the area, negatively correlated in 9%, and are positively correlated in the arid and semi-arid areas, while negatively correlated in the humid and subhumid areas. The significant negative correlation between NDVI and drought at high altitudes may be due to the fact that Gramineae vegetation is more sensitive to drought, with heat being more affected than water. (2) Urbanization has a relatively obvious impact on the distribution of drought. Extreme drought mainly occurs in the middle and upper reaches of the Wei River; severe drought mainly occurs in the central area of the Guanzhong Plain centered on Xi'an; the central area of the Loess Plateau; and the surrounding areas of the Zhengzhou-centered Central Plains City Group. (3) The NDVI showed an upward trend from 2003 to 2020, indicating an increase in vegetation density or an expansion of vegetation coverage. From the temporal trend, SPEI decreased at a rate of −0.17/decade, indicating that the entire watershed has a drought trend on an annual scale. (4) Spring NDVI is more sensitive to the water supply provided by SPEI-1, while the positive correlation between SPEI and NDVI begins to rise in June and reaches its peak in July, then starts to decline in August. In autumn and winter, NDVI is more sensitive to 3–6-month accumulated drought. (5) From the dynamic transmission laws of different levels of positive correlation, the positive impact of the 3-month accumulated drought on NDVI is most significant, and the influence of SPEI-1 on the negative correlation between SPEI and NDVI is most significant. This paper aims to clarify the sensitivity of vegetation to different time-scale droughts, provide a basis for alleviating drought in the YRB, and promote sustainable development of ecological environmental protection. The research findings enable us to gain a profound insight into the responsiveness of vegetation growth to drought in the context of global warming and offer a valuable theoretical foundation for devising pertinent measures to alleviate stress on vegetation growth in regions prone to frequent droughts. [ABSTRACT FROM AUTHOR]

Rapid urbanization worldwide has significantly altered urban climates, creating a need to balance urban growth with thermal environmental quality for sustainable development. This study examines the relationship between land surface temperature (LST) and urban characteristics, particularly focusing on how green cover can mitigate urban heat and how air pollution can increase temperatures. Recognizing the predictive value of LST for urban heat island (UHI) intensity, we analyzed three distinct U.S. cities—Chicago, San Francisco, and Phoenix—each characterized by unique climate and urban planning features. This study investigates the relationship between atmospheric pollutants (SO2, NO2, CO, O3) and the Normalized Difference Vegetation Index (NDVI) with land surface temperature (LST) using regression and correlation analyses. The analysis aims to elucidate how changes in atmospheric pollutants and NDVI affect variations in land surface temperature. Regression analysis is employed to estimate the coefficients of independent variables and quantify their impact on LST. Correlation analysis assesses the linear relationships between variables, providing insights into their pairwise associations. The study also examines multicollinearity between independent variables to identify potential confounding factors. The results reveal significant associations between atmospheric pollutants, NDVI, and land surface temperature, contributing to our understanding of the environmental factors influencing LST dynamics and informing climate change mitigation strategies. The observed inconsistencies in correlations across cities highlight the importance of the local context in environmental studies. Understanding these variations can aid in developing tailored urban planning policies that consider unique city characteristics for more effective climate resilience. Furthermore, a positive association was consistently obtained between pollutants and LST, indicating that increased pollution levels contribute to higher surface temperatures across different urban settings. [ABSTRACT FROM AUTHOR]

Urban heat poses significant challenges in rapidly developing cities, particularly in countries like Bangladesh. This study investigates the cooling effects of urban green spaces in Rajshahi city, addressing a critical research gap in developing urban contexts. We examined the relationships among urban vegetation, heat mitigation, and temperature variables using the InVEST Urban Cooling Model and spatial analysis techniques. This study focused on three key relationships: Normalized Difference Vegetation Index (NDVI) and Heat Mitigation Index (HMI), HMI and Land Sur face Temperature (LST), and HMI and Air Temperature (AT). Analysis revealed a strong positive correlation between NDVI and HMI, indicating the effectiveness of vegetation in enhancing urban cooling. A robust inverse relationship between HMI and LST was observed (R2 = 0.78, r = −0.88), with every 0.1 unit increase in HMI corresponding to a 0.53 °C decrease in LST. The HMI−AT relationship showed an even stronger correlation (R2 = 0.84, r = −0.87), with each unit increase in HMI associated with a 2.80 °C decrease in air temperature. These findings quantify the significant role of urban green spaces in mitigating heat and provide valuable insights for urban planning in developing cities, underscoring the importance of integrating green infrastructure into urban-development strategies to combat urban heat and improve livability. [ABSTRACT FROM AUTHOR]

The impact of changing climate on vegetation in dryland is a prominent focus of global research. As a typical arid region in the world, Central Asia is an ideal area for studying the associations between climate and arid-area vegetation. Utilizing data from the European Centre for Medium-Range Weather Forecasts fifth-generation reanalysis (ECMWF ERA-5) and normalized difference vegetation index (NDVI) datasets, this study investigates the spatio-temporal variation characteristics of the NDVI in Central Asia. It quantitatively assesses the contribution rates of climatic factors to vegetation changes and elucidates the impact of an increased vapor pressure deficit (VPD) on vegetation changes in Central Asia. The results indicate that the growing seasons' NDVI exhibited a substantial increase in Central Asia during 1982–2015. Specifically, there was a pronounced "greening" process (0.012/10 yr, p < 0.05) from 1982 to 1998. However, an insignificant "browning" trend was observed after 1998. Spatially, the vegetation NDVI in the growing seasons exhibited a pattern of "greening in the east and browning in the west" of Central Asia. During spring, the dominant theme was the "greening" of vegetation NDVI, although there was noticeable "browning" observed in southwest region of Central Asia. During summer, the "browning" of vegetation NDVI further expanded eastward and impacted the entire western Central Asia in autumn. According to the estimated results computed via the partial differential equation method, the "browning" trend of vegetation NDVI during the growing seasons was guided by increased VPD and decreased rainfall in western Central Asia. Specifically, the increased VPD contributed 52.3% to the observed vegetation NDVI. Atmospheric drought depicted by the increase in VPD significantly lowers the "greening" trend of vegetation NDVI in arid regions, which further aggravates the "browning" trend of vegetation NDVI. [ABSTRACT FROM AUTHOR]

Amidst escalating global energy demands, the advancement and utilization of renewable energy sources have emerged as critical strategies for addressing environmental concerns and alleviating energy crises. Among them, wind power, as a renewable and clean energy source, has been widely applied and developed in China. However, the construction of wind farms may have some impact on vegetation cover and soil properties. This study aims to assess the impact of wind farm construction on vegetation cover and soil characteristics, thereby offering a scientific foundation for the sustainable management of wind farm development sites. The present study was carried out in the area of Jingzhushan wind farm in Linxiang City, Hunan Province, to examine the trends of the normalized difference vegetation index (NDVI), the fractional vegetation cover (FVC), and the indexes expressing the physicochemical properties of the soil in this area. The results showed the following: (1) The NDVI of the wind farm for the three periods was 0.742 in 2013, 0.770 in 2016, and 0.758 in 2023, respectively. According to the analysis of the index of FVC, it can be seen that the trend of the FVC of the study area for the three periods was basically the same as that of the NDVI. The average value of FVC was 0.754 in 2013, 0.791 in 2016, and 0.769 in 2023. This indicated that the vegetation cover in the early stage of wind farm construction (2013) was lower than that in the late stage of operation (2016, 2023), and it also suggested that the vegetation cover gradually recovered over time. (2) Compared with natural ecosystems, both altitude and wind farm construction significantly affected the organic carbon, the total nitrogen, the effective phosphorus, and the rapidly available potassium in the soil. At the same altitude, these four soil indicators in the area where the wind turbines were constructed had significantly lower levels compared with the control (CK), which indicated a decrease in soil fertility—the closer to the turbine construction area, the lower the levels of each indicator. In addition, soil pH did not change significantly during the construction of the wind farm. The analysis and comparison of various data showed that the construction and operation of wind farms can have an impact on local vegetation cover, and it had a significant negative impact on soil properties. Reasonable measures are needed to protect vegetation and soil to achieve the sustainable development of the ecological environment. [ABSTRACT FROM AUTHOR]

Investigation of cities' spatiotemporal dynamics, including vegetation and urban areas, is of utmost importance for understanding ecological balance, urban planning, and sustainable development. This study investigated the dynamic interactions between vegetation, settlement patterns, and surface urban heat islands (SUHIs) in Istanbul using spatiotemporal hotspot analysis. Utilizing Landsat satellite imagery, we applied the Getis-Ord Gi* statistic to analyze Land Surface Temperature (LST), Urban Index (UI), and Normalized Difference Vegetation Index (NDVI) across the city. Using satellite images and the Getis-Ord Gi* statistic, this research investigated how vegetation and urbanization impact SUHIs. Based on the main results, mean NDVI, UI, and LST values for 2009 and 2017 were analyzed, revealing significant vegetation loss in 37 of Istanbul's 39 districts, with substantial urbanization, especially in the north, due to new infrastructure development. On the other hand, hotspot analysis was conducted on normalized NDVI, UI, and LST images by analyzing 977 neighborhoods. Results showed a significant transformation of green areas to non-significant classes in NDVI, high urbanization in UI, and the formation of new hot areas in LST. SUHIs were found to cluster in areas with increasing residential and industrial activities, highlighting the role of urban development on SUHI formation. This research can be applied to any region since it offers crucial perspectives for decision-makers and urban planners aiming to mitigate SUHI effects through targeted greening strategies and sustainable urban development. By integrating environmental metrics into urban planning, this study underscores the need for comprehensive and sustainable approaches to enhance urban resilience, reduce environmental impact, and improve livability in Istanbul. [ABSTRACT FROM AUTHOR]

As global climate change intensifies and human activities escalate, changes in vegetation cover, an important ecological indicator, hold significant implications for ecosystem protection and management. Shandong Province, a critical agricultural and economic zone in China, experiences vegetation changes that crucially affect regional climate regulation and biodiversity conservation. This study employed normalized difference vegetation index (NDVI) data, combined with climatic, topographic, and anthropogenic activity data, utilizing trend analysis methods, partial correlation analysis, and Geodetector to comprehensively analyze the spatiotemporal variations and primary driving factors of vegetation cover in Shandong Province from 2001 to 2020. The findings indicate an overall upward trend in vegetation cover, particularly in areas with concentrated human activities. Climatic factors, such as precipitation and temperature, exhibit a positive correlation with vegetation growth, while land use changes emerge as one of the key drivers influencing vegetation dynamics. Additionally, topography also impacts the spatial distribution of vegetation to a certain extent. This research provides a scientific basis for ecological protection and land management in Shandong Province and similar regions, supporting the formulation of effective vegetation restoration and ecological conservation strategies. [ABSTRACT FROM AUTHOR]

Drought may considerably decrease the growth and yield of potatoes. Small tubers may have lower performance and be more sensitive to abiotic stresses than larger tubers. Since an increase in drought areas may be expected, the development of potato varieties with drought tolerance has become necessary. Two-year greenhouse experiments were conducted to test the drought tolerance of potato breeding lines (C103, C107, C20) with great osmotic stress tolerance. Minitubers with diameters of 25–35, 20–24, 15–19 and 10–14 mm were planted. Treatments were the optimal irrigated control (100%) and moderate and severe drought (60% and 20% of optimum water supply). To study the after-effects of drought, tubers from different treatments were planted separately the following year because seed tuber priming may increase drought tolerance. Seed tubers (25–35 mm), two irrigation treatments (control and severe drought), and two control cultivars were used in the second year. We observed the rate of emergence from day-after-planting (DAP) 20 to 30 and flowering from 48 to 54. NDVI measurements were performed on the DAP35-45-75. Plant height and fresh weight of aboveground biomass (AGB) were recorded on DAP76. Harvested tubers were counted, weighed, and size-categorized, and then the number and fresh tuber yield per plant (TN and TY) were calculated. Stress indices (SI) were calculated as percentages of the results of control plots to compare the responses of genotypes to drought stress. We found that each breeding line showed adequate drought tolerance, although only the C103 and C107 breeding lines were stable in in vivo conditions. SI values for tuber number/tuber yield were 103/57; 102/63; 83/52; 80/58 and 55/41 in C103, C107, C20, 'Boglárka' and 'Desiree' (the last two were control varieties), respectively. The size of the seed tuber significantly affected each character, and usually minitubers larger than 20 mm performed better than smaller ones. No significant after-effect of drought stress on the next generation was found. Although we found a positive correlation (r = 0.83) between NDVI values and yield parameters, the correlations in our study were not consistent in all genotypes and water treatments. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) provide images at decametric spatial resolutions. Their flexibility, efficiency, and low cost make it possible to apply UAV remote sensing to multisensor data acquisition. In this frame, the present study aims at employing RGB UAV images (at a 3 cm resolution) and multispectral images (at a 16 cm resolution) with related vegetation indices (VIs) for mapping surfaces according to their illumination. The aim is to map land cover in order to access temperature distribution and compare NDVI and MTVI2 dynamics as a function of their illuminance. The method, which is based on a linear discriminant analysis, is validated at different periods during the phenological cycle of the crops in place. A model based on a given date is evaluated, as well as the use of a generic model. The method provides a good capacity of separation between four classes: vegetation, no-vegetation, shade, and sun (average kappa of 0.93). The effects of agricultural practices on two adjacent plots of maize respectively submitted to conventional and conservation farming are assessed. The transition from shade to sun increases the brightness temperature by 2.4 °C and reduces the NDVI by 26% for non-vegetated surfaces. The conservation farming plot is found to be 1.9 °C warmer on the 11th of July 2019, with no significant difference between vegetation in the sun or shade. The results also indicate that the NDVI of non-vegetated areas is increased by the presence of crop residues on the conservation agriculture plot and by the effect of shade on the conventional plot which is different for MTVI2. [ABSTRACT FROM AUTHOR]

Green vegetation is one of the main objects of ecological environment restoration and protection, objectively reflecting the quality of regional ecological environments. Studying its spatial distribution characteristics is of great significance to the formulation of ecological environment restoration policies. Based on data on urban green vegetation in China from 2000 to 2022, this study attempts to analyze the destruction and protection patterns of urban green vegetation in China from the perspectives of total changes in green vegetation contraction and growth and spatial evolution characteristics and trends, and it explores the driving factors affecting the change in green vegetation area. The results show the following: (1) Green vegetation growth and contraction occurred alternately in China from 2000 to 2022. Vegetation contraction showed a "point–line–plane" evolution pattern, forming a contraction stage of point-like aggregation, linear series, and planar spread. Vegetation growth has always presented a frontal pattern. (2) The growth and contraction of green vegetation in China showed a north–south differentiation phenomenon. The vegetation contraction phenomenon spread in the Central Plains urban agglomeration and its surrounding areas and showed an expanding trend. The growth trend is obviously moving northward, mainly concentrated in Inner Mongolia, Ningxia, Gansu, Xinjiang, and other northern provinces, which also coincides with the key ecological restoration policies in northern China in recent years. (3) City scale, economic level, population scale, agro-industrial structure, and water resources content have significant effects on the spatial distribution of green vegetation. [ABSTRACT FROM AUTHOR]

In urban settings, trees and greenery play a vital role in environmental well-being and community vitality. This study explores the impact of Miami-Dade County's tree-planting initiative on urban greenness and considers the influence of climate dynamics. Using Landsat data from 2006 to 2019, we find stable overall greenness, with 5.64% of the Census blocks exhibiting significant changes. Seasonal analysis reveals winter as prominent, with 61.47% of Census blocks showing increased greenness. Temperature and precipitation, especially post-2010, correlate with greenness changes. Despite a reported increase in tree cover from 14% to 20%, our findings show only 5–6% of Census blocks with statistically significant changes, highlighting the complexity of achieving substantial improvements in green canopy coverage. The study raises questions about the efficacy of large-scale tree-planting initiatives in densely urbanized areas when human factors are not well understood. Implications for urban planning stress the importance of preserving green spaces and informed decision-making for enhancing vegetation cover in Miami-Dade County, emphasizing the need to consider local conditions, seasonal variations, policies, and human factors in urban greening efforts. [ABSTRACT FROM AUTHOR]

This paper presents the implementation of statistical and remote sensing techniques to analyze the spectral response, grain yield, and infestation of fall armyworm (Spodoptera frugiperda) in corn (Zea mays) based on the application of edaphic and foliar treatments with silicon, comparing the results with those reported in the literature where it has been demonstrated that the incorporation of this nutrient in different crops improves the activity of the enzyme nitrate reductase and dry matter weight gain. The results show that the foliar application of silicon tends to increase grain production in the crop, while the soil treatment does not improve yield. Similarly, foliar silicon application improves the Normalized Difference Vegetation Index, which improves plant health and could be correlated with higher grain yield of the crop. An inverse correlation was detected between the use of foliar silicon and the Normalized Difference Water Index and a direct relationship in the case of direct field application. As for the analysis of the data to verify the influence of the use of silicon on fall armyworm infestation, no statistically significant evidence was found that would lead to the conclusion that the application of this element, whether in soil or foliar form, could lead to a decrease in crop infestation. [ABSTRACT FROM AUTHOR]

As vegetation plays a critical role in terrestrial ecosystems, understanding its status and variation is vital for preserving the stability of an ecosystem. Central Asia serves as a representative example of an arid and semi-arid region characterized by sparse vegetation and poor soils, making its vegetation particularly fragile and sensitive. To investigate the vegetation condition in the region, this study examined the spatial and temporal characteristics of vegetation variation from 2001 to 2020, utilizing the normalized difference vegetation index (NDVI) as an indicator. Meanwhile, trend analysis, Mann–Kendall abrupt change point test, geodetector, and correlation analysis were used to quantitatively analyze the natural and anthropogenic drivers of these variations over the past two decades. The results suggest that vegetation coverage in Central Asia was relatively low, with an annual average NDVI of 0.16 over the past 20 years. Moreover, the spatial distribution of NDVI in Central Asia exhibited significant spatial heterogeneity, with vegetation coverage declining from north to south and from east to west. Furthermore, the NDVI exhibited a slightly increasing trend during the period of 2001 to 2020 with an increased rate of 0.00025/yr. However, we detected an abrupt change point in vegetation dynamics in Central Asia around 2010, which indicated a significant shift in vegetation variation in the region. Land-use type has a great influence on the spatial heterogeneity of NDVI in Central Asia, which can explain 46% of the vegetation distribution in this region. Moisture factors such as precipitation and soil water content followed with 35% and 32% contributions, respectively. Regarding the temporal variation of NDVI, it is mainly driven by the fluctuation in precipitation, with the degree of influence of precipitation on NDVI varying for different regions in various geographical conditions. This study offers a more comprehensive insight into the spatial and temporal dynamics of NDVI in Central Asia and indicates that precipitation plays a significant role in driving the spatial heterogeneity and temporal variation of NDVI. These findings are essential for predicting vegetation changes in arid regions under future environmental conditions and formulating effective strategies to prevent and alleviate vegetation degradation. [ABSTRACT FROM AUTHOR]

The Tibet Plateau of Qinghai supports complex vegetation types that are ecologically fragile and sensitive to climatic factors. Therefore, it is important to understand the changes in vegetation and the factors responsible for these changes and to maintain the ecosystem balance and promote sustainable development in the region. Therefore, this paper is based on annual SPOT/VEG NDVI (Normalized Difference Vegetation Index) data, land use data, topographic data, temperature data, and precipitation data from 1999 to 2019. The spatiotemporal variation in the NDVI over the Tibetan Plateau in the last 21 years and its response to different driving factors were investigated by using the Theil–Sen slope method, Mann–Kendall test, partial correlation analysis, and geographical detector method. The results showed that (1) the vegetation coverage on the Qinghai–Tibet Plateau showed an increasing trend from 1999 to 2019, with increases in approximately 67.00% of the plateau area. (2) The spatial differences in vegetation coverage were large; notably, low-density vegetation areas decreased obviously, moderate-density vegetation areas accounted for approximately 50% of the total area, high-density vegetation areas were the least common, and the overall growth rate was significant. (3) The NDVI was positively correlated with temperature and precipitation, and a positive correlation was observed in more than 66% of the region. (4) The order of the influence of single driving factors on the NDVI was as follows: precipitation > soil type > altitude > temperature > gradient > slope > population density > GDP. (5) The combined effect of the factors was significantly higher than that of single driving factors, with a notable nonlinear influence. The interactions between meteorological factors, such as precipitation, and topographic factors, such as altitude, were important, with a q-value over 0.79. The results of this study provide some methodological support for the ecological conservation of the Tibetan Plateau, and at the same time establish a scientific and reasonable strategy for vegetation restoration. [ABSTRACT FROM AUTHOR]

Precipitation is a critical driver of vegetation productivity and dynamics in dryland environments, especially in areas with intense livestock farming. Availability and access to accurate, reliable, and timely rainfall data are essential for natural resources management, environmental monitoring, and informing hydrological rainfall-runoff models. Gauged precipitation data in drylands are often scarce, fragmented, and with low spatial resolution; therefore, satellite-estimated precipitation becomes a valuable dataset for overcoming this constraint. Using statistical indices, we compared satellite-derived precipitation data from four products (CHIRPS, GPM, TRMM, and PERSIANN-CDR) against gauged data at different temporal scales (daily, monthly, and yearly). Spatial correlations were calculated for GPM and CHIRPS estimates against interpolated gauged precipitation. We then estimated NDVI response to Antecedent Accumulated Precipitation (AAP) for 1, 3, 6, 9, and 12 months of four major vegetation types typical of the region. Statistical metrics varied with temporal scales being highest and acceptable for periods of 1 month or 1 year. At monthly scale GPM presented the best Pearson's Correlation Coefficient (r), Root Mean Square Error (RMSE) and RMSE-observations standard deviation ratio (RSR) and CHIRPS resulted in lower Mean Error (ME) and Bias. On an annual basis CHIRPS showed the best adjustment for all indicators except for r. NDVI responses to 3 months of AAP were significant for all vegetation types in the study area. The findings of this study show that estimated precipitation data from GPM and CHIRPS satellites are accurate and valuable as a tool for analysing the relationships between precipitation and vegetation in the drylands of Mendoza. [ABSTRACT FROM AUTHOR]

The temperature vegetation dryness index (TVDI) is widely used for the monitoring of global or regional drought because of its strong drought-monitoring capabilities and ease of implementation. However, the temporal errors in the land surface temperature (LST) and normalized difference vegetation index (NDVI) can affect warm and cold edges, thus determining the quality of the TVDI, especially in regions affected by climate change, such as Shandong Province. This paper explores this issue in the region in 2011, using daily MODIS MOD09GA and MOD11A1 data products. For each image acquisition time, the warm and cold edges of the NDVI–LST were extracted based on the NDVI, derived from red and near-infrared reflectance data, and the LST, derived from the MOD11A1 dataset. Then, the variations in the warm and cold edges with the LST and NDVI were analyzed. Subsequently, the influence of warm and cold edges, based on the daily values of the temperature, NDVI and precipitation during the observed period, was assessed using a linear regression. The soil moisture (SM) data obtained from the Global Land Data Assimilation System (GLDAS) datasets and the crop water stress index (CWSI) obtained from the MOD16A2 products were used for the assessment. The spatial and temporal variations in drought in Shandong Province from 2011 to 2020 were measured based on Theil–Sen median trend analysis and the Mann–Kendall test. The results show that apparently random variations were evident in the temporal evolution of the slope of the warm edge, indicating that daily data were appropriate to determine the boundary of the warm edge. Daily data were also appropriate to determine the boundary of the cold edge in a similar way. Additionally, the temperature, NDVI and precipitation in this region affected by climate change had a negative correlation with the slope and a positive correlation with the intercept. The validation results show that there was a significant negative correlation between the observed TVDI and GLDAS soil moisture values (R2 > 0.62) in 12 scatter plots. Therefore, we deduced that the monthly or yearly TVDI product produced by the daily MODIS data has a higher precision than that produced by 8-day or monthly data in regions affected by climate change. The spatial and temporal variations show that the trend of slight and moderate droughts first increased and then decreased, and, in particular, some areas presented severe drought from 2011 to 2015. The results obtained in this study are important for the scheduling of irrigation and drought warnings. [ABSTRACT FROM AUTHOR]

Drought causes significant losses in vegetation net primary productivity (NPP). However, the lack of real-time, large-scale NPP data poses challenges in analyzing the relationship between drought and NPP. Solar-induced chlorophyll fluorescence (SIF) offers a real-time approach to monitoring drought-induced NPP dynamics. Using two drought events in the Huang–Huai–Hai Plain from 2010 to 2020 as examples, we propose a new SIF/NPP ratio index to quantify and evaluate SIF's capability in monitoring drought-induced NPP dynamics. The findings reveal distinct seasonal changes in the SIF/NPP ratio across different drought events, intensities, and time scales. SIF demonstrates high sensitivity to commonly used vegetation greenness parameters for NPP estimation (R2 > 0.8, p < 0.01 for SIF vs NDVI and SIF vs LAI), as well as moderate sensitivity to land surface temperature (LST) and a fraction of absorbed photosynthetically active radiation (FAPAR) (R2 > 0.5, p < 0.01 for SIF vs FAPAR and R2 > 0.6, p < 0.01 for SIF vs LST). However, SIF shows limited sensitivity to precipitation (PRE). Our study suggests that SIF has potential for monitoring drought-induced NPP dynamics, offering a new approach for real-time monitoring and enhancing understanding of the drought–vegetation productivity relationship. [ABSTRACT FROM AUTHOR]

Frequent droughts may have negative influences on the ecosystem (i.e., terrestrial vegetation) under a warming climate condition. In this study, the linear regression method was first used to analyze trends in vegetation change (normalized difference vegetation index (NDVI)) and drought indices (Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI)). The Pearson Correlation analysis was then used to quantify drought impacts on terrestrial vegetation in the Weihe River Basin (WRB); in particular, the response time of vegetation to multiple time scales of drought (RTVD) in the WRB was also investigated. The trend analysis results indicated that 89.77% of the area of the basin showed a significant increasing trend in NDVI from 2000 to 2019. There were also significant variations in NDVI during the year, with the highest rate in June (0.01) and the lowest rate in January (0.002). From 2000 to 2019, SPI and SPEI at different time scales in the WRB showed an overall increasing trend, which indicated that the drought was alleviated. The results of correlation analysis showed that the response time of vegetation to drought in the WRB from 2000 to 2019 was significantly spatially heterogeneous. For NDVI to SPEI, the response time of 12 months was widely distributed in the north; however, the response time of 24 months was mainly distributed in the middle basin. The response time of NDVI to SPI was short and was mainly concentrated at 3 and 6 months; in detail, the response time of 3 months was mainly distributed in the east, while a response time of 6 months was widely distributed in the west. In autumn and winter, the response time of NDVI to SPEI was longer (12 and 24 months), while the response time of NDVI to SPI was shorter (3 months). From the maximum correlation coefficient, the response of grassland to drought (SPEI and SPI) at different time scales (i.e., 6, 12, and 24 months) was higher than that of cultivated land, forestland, and artificial surface. The results may help improve our understanding of the impacts of climatic changes on vegetation cover. [ABSTRACT FROM AUTHOR]

The Lluta River is the northernmost coastal wetland in Chile, representing a unique ecosystem and an important source of water in the extremely arid Atacama Desert. During peak season, the wetland is home to more than 150 species of wild birds and is the first stopover point for many migratory species that arrive in the country along the Pacific migratory route, thereby representing a priority site for avian influenza virus (AIV) surveillance in Chile. The aim of this study was to determine the prevalence of influenza A virus (IAV) in the Lluta River wetland, identify subtype diversity, and evaluate ecological and environmental factors that drive the prevalence at the study site. The wetland was studied and sampled from September 2015 to October 2020. In each visit, fresh fecal samples of wild birds were collected for IAV detection by real-time RT-PCR. Furthermore, a count of wild birds present at the site was performed and environmental variables, such as temperature, rainfall, vegetation coverage (Normalized Difference Vegetation Index—NDVI), and water body size were determined. A generalized linear mixed model (GLMM) was built to assess the association between AIV prevalence and explanatory variables. Influenza positive samples were sequenced, and the host species was determined by barcoding. Of the 4349 samples screened during the study period, overall prevalence in the wetland was 2.07% (95% CI: 1.68 to 2.55) and monthly prevalence of AIV ranged widely from 0% to 8.6%. Several hemagglutinin (HA) and neuraminidase (NA) subtypes were identified, and 10 viruses were isolated and sequenced, including low pathogenic H5, H7, and H9 strains. In addition, several reservoir species were recognized (both migratory and resident birds), including the newly identified host Chilean flamingo (Phoenicopterus chilensis). Regarding environmental variables, prevalence of AIV was positively associated with NDVI (OR = 3.65, p < 0.05) and with the abundance of migratory birds (OR = 3.57, p < 0.05). These results emphasize the importance of the Lluta wetland as a gateway to Chile for viruses that come from the Northern Hemisphere and contribute to the understanding of AIV ecological drivers. [ABSTRACT FROM AUTHOR]

Walnut (Juglans regia) planting is the main economic pillar industry in southern Xinjiang. Based on the Google Earth Engine (GEE) cloud platform, the NDVI maximum synthesis method was used to estimate changes in the walnut cultivation area in Ganquan Town, South Xinjiang, from 2017 to 2021. The simultaneous difference between NDVI and meteorological conditions was also used to monitor the growth and correlation analysis of walnuts from April to September 2021. To improve the classification accuracy of the extracted walnut plantation area, Sentinel-2 image data were selected, and features were trained using the random forest algorithm, and by combining topographic features, texture features, NDVI, and EVI. The results show that, compared with Statistical Yearbook data, the average error of the extracted walnut planted area is less than 10%, the overall classification accuracy is 92.828%, the average kappa coefficient is 90.344%, and the average walnut classification accuracy is 94.4%. The accuracy of the data was significantly improved by adding vegetation indices EVI and NDVI compared with the single vegetation index. An analysis of the results from monitoring comparative growth shows that the growth of walnuts in Ganquan was better during the hardcore and oil transformation stages compared with 2020, and in the fruit development stage, the growth was the same as in 2020, and overall, the growth of walnuts in 2021 was better than in previous years. [ABSTRACT FROM AUTHOR]

In the EU, aquaculture ponds cover an area of 360,000 ha and are a crucial part of the rural landscape. As many ecosystem services (e.g., habitats for protected wildlife, nutrient cycling, etc.) are correlated with the proportion of reed beds relative to open-water areas, it is important in environmental studies to be able to accurately estimate the extent and the temporal dynamics of reed cover. Here, we propose a method for mapping reed cover in fish ponds from freely available Sentinel-2 imagery using the normalized difference vegetation index (NDVI), which we applied to Hungary, the third largest carp producer in the EU. The dynamics of reed cover in Hungarian fish ponds mapped using satellite imagery show a high degree of agreement with the ground-truth points, and when compared with data reported in the annual aquaculture reports for Hungary, it was found that the calculation of reed cover based on the NDVI-based approach was more consistent than the estimates provided in the report. We discuss possible applications of this remote sensing technique in estimating reed-like vegetation cover in fish ponds and the possible use of the results for climate change studies and ecosystem services assessment. [ABSTRACT FROM AUTHOR]

Several earth science investigations depend heavily on knowing the surface energy budget and determining surface temperature. The primary factor affecting the energy balance in the surface physical processes of the planet is the land surface temperature (LST). Even in the case of small-scale green areas like local parks, plants have a significant impact on the climate of cities. The goal of this study was to estimate the construction-related impacts of the Karizland green town (green belt) on the LST of its surroundings over time, for the years 2013 (before construction began), 2015, 2020 and 2022 (after construction was completed). LST values and hot spot analyses were employed for thermal condition evaluation purposes on Landsat-8 satellite images, and normalized difference vegetation index (NDVI) and fractional vegetation cover (FVC) indices were used for examining the vegetation change. The results showed that after the establishment of the green town, the mean NDVI and FVC grew by 275% and 950%, respectively, compared to the initial period, which resulted in the addition of approximately 208.35 ha of green space to the study area. In this regard, the results showed that after these changes, compared to the first period, the mean LST decreased by 8%. In addition, the area of the class of hotspot analysis with less than 90% confidence increased by 9%. The results illustrated that almost 20% of the data in the LST range was below 55 °C in 2013, near 57 °C in 2015, and around 51 °C in 2020 and 2022. The results also showed a negative relationship between the distance from the established settlement and the values of NDVI and FVC in 2022 of 91% and 89% and in 2020 of 67% and 69%, respectively. Every year, LST has had a significant negative relationship with the NDVI and FVC of that year and a positive relationship with the LST of the following years, such that the correlation decreases in later years. In order to control LST and the temperature surrounding cities, this research strongly advises managers to develop these green towns. [ABSTRACT FROM AUTHOR]

The Tibet Autonomous Region (TAR) is located in the mid-latitude and high-cold regions, and the ecological environment in most areas is fragile. Studying its surface vegetation coverage can identify the ecosystem's development trends and provide a specific contribution to global environmental change. The normalized difference vegetation index (NDVI) can better reflect the coverage of surface vegetation. Therefore, based on remote sensing data with a resolution of 1 km2, air temperature, precipitation, and other data in the same period in the study area from 1998 to 2019, this paper uses trend analysis, F-significance tests, the Hurst index, and the Geodetector model to obtain the spatial distribution, change characteristics, and evolution trends of the NDVI in the TAR in the past 22 years. At the same time, the quantitative relationship between natural and human factors and NDVI changes is also obtained. The study results show that the NDVI in the southern and southeastern parts of the TAR is higher, with mean values greater than 0.5 showing that vegetation cover is better. The NDVI in the western and northwestern parts of the TAR is lower, with mean values less than 0.3, indicating vegetation cover is worse. NDVI in the TAR showed an overall increasing trend from 1998 to 2019 but a decreasing trend in ridgelines, snow cover, and glacier-covered areas. The areas where NDVI values show a trend of increasing and then decreasing in the future account for 53.69% of the total area of the TAR. The most crucial factor affecting NDVI changes in the TAR is soil type, followed by influencing factors such as vegetation cover type, average annual air temperature, and average annual precipitation. The influence of natural elements is generally more significant than anthropogenic factors. The influencing factors have synergistic effects, and combining anthropogenic factors and other factors will show mutual enhancement and non-linear enhancement relationships. This study provides a theoretical basis for natural resource conservation, ecosystem restoration, and sustainable human development strategies in the TAR. [ABSTRACT FROM AUTHOR]

Precision agriculture (PA) practices based on the use of sensors and vegetation indices have great potential for optimizing herbicide use and improving weed management in field crops. The objective of this research was to evaluate the efficacy of commercial herbicide products and their selectivity in durum wheat by measuring the Normalized Difference Vegetation Index (NDVI). Field trials were conducted in Velestino and Kozani, Greece (2020–2021 and 2021–2022) in four site-years with the following treatment list: untreated control (T1), 2,4-D at 300 and 600 g a.e. ha−1 (T2 and T3, respectively), pyroxsulam + florasulam at 18.82 + 3.71 g a.i. ha−1 + cloquintocet-mexyl at 18.82 g a.i. ha−1 (T4), and mesosulfuron-methyl + iodosulfuron-methyl-sodium at 15 + 3 g a.i. ha−1 + mefenpyr-diethyl at 45 g a.i. ha−1 (T5). Site-years and treatments affected weed NDVI, weed biomass, crop NDVI, and grain yield (p ≤ 0.05). At Kozani, weed NDVI was lowest in T4 plots in 2020–2021 (0.31) and 2021–2022 (0.33). Treatments T4 and T5 resulted in lowest weed biomass in 2020-2021 (14–16 g m−2) and 2020-2021 (19–22 g m−2). At Velestino, T3 reduced weed biomass by 92 and 87% when compared to T5 in 2020–2021 and 2021–2022, respectively. Approximately, 67% and 73% of the variability in weed biomass in 2020–2021 and 2021–2022, respectively, at Kozani could be explained by weed NDVI. These parameters were strongly correlated in Velestino (R2 ≥ 90%). Low crop NDVI at Kozani indicated herbicide injury in T3 plots, confirmed by yield losses. During 2020-2021, yield was 30, 38, and 40% higher in T4 plots than in T2, T1, and T3 plots, respectively. At Velestino, yield in T1 plots was 25, 27, 27, and 29% lower than in T2, T4, T5, and T3 plots, respectively, in 2020–2021. Similar results were obtained in 2021–2022. The current study indicates that NDVI can be used as a reliable, non-subjective indicator of herbicide efficacy and selectivity in winter cereals. The methodology used in this work should also be evaluated in other crops and under different soil and climatic conditions. [ABSTRACT FROM AUTHOR]

Vegetation indices provide information for various precision-agriculture practices, by providing quantitative data about crop growth and health. To provide a concise and up-to-date review of vegetation indices in precision agriculture, this study focused on the major vegetation indices with the criterion of their frequency in scientific papers indexed in the Web of Science Core Collection (WoSCC) since 2000. Based on the scientific papers with the topic of "precision agriculture" combined with "vegetation index", this study found that the United States and China are global leaders in total precision-agriculture research and the application of vegetation indices, while the analysis adjusted for the country area showed much more homogenous global development of vegetation indices in precision agriculture. Among these studies, vegetation indices based on the multispectral sensor are much more frequently adopted in scientific studies than their low-cost alternatives based on the RGB sensor. The normalized difference vegetation index (NDVI) was determined as the dominant vegetation index, with a total of 2200 studies since the year 2000. With the existence of vegetation indices that improved the shortcomings of NDVI, such as enhanced vegetation index (EVI) and soil-adjusted vegetation index (SAVI), this study recognized their potential for enabling superior results to those of NDVI in future studies. [ABSTRACT FROM AUTHOR]

Revealing the spatial dynamics of vegetation change in Chongqing and their driving mechanisms is of major value to regional ecological management and conservation. Using several data sets, including the SPOT Normalized Difference Vegetation Index (NDVI), meteorological, soil, digital elevation model (DEM), human population density and others, combined with trend analysis, stability analysis, and geographic detectors, we studied the pattern of temporal and spatial variation in the NDVI and its stability across Chongqing from 2000 to 2019, and quantitatively analyzed the relative contribution of 18 drivers (natural or human variables) that could influence vegetation dynamics. Over the 20-year period, we found that Chongqing region's NDVI had an annual average value of 0.78, and is greater than 0.7 for 93.52% of its total area. Overall, the NDVI increased at a rate of 0.05/10 year, with 81.67% of the areas undergoing significant expansion, primarily in the metropolitan areas of Chongqing's Three Gorges Reservoir Area (TGR) and Wuling Mountain Area (WMA). The main factors influencing vegetation change were human activities, climate, and topography, for which the most influential variables respectively were night light brightness (NLB, 51.9%), annual average air temperature (TEM, 47%), and elevation (ELE, 44.4%). Furthermore, we found that interactions between differing types of factors were stronger than those arising between similar ones; of all pairwise interaction types tested, 92.9% of them were characterized by two-factor enhancement. The three most powerful interactions detected were those for NLB ∩ TEM (62.7%), NLB ∩ annual average atmospheric pressure (PRS, 62.7%), and NLB ∩ ELE (61.9%). Further, we identified the most appropriate kind or range of key elements shaping vegetation development and dynamics. Altogether, our findings can serve as a timely scientific foundation for developing a vegetative resource management strategy for the Yangtze River basin that duly takes into account local climate, terrain, and human activity. [ABSTRACT FROM AUTHOR]

Alfalfa (Medicago sativa L.) is one of the most widely planted forages due to its useful characteristics. Although alfalfa spatial distribution is an important source of basic data, manual surveys incur high survey costs, require large workloads and confront difficulties in collecting data over large areas; remote sensing compensates for these shortcomings. In this study, the time-series variation characteristics of different vegetation types were analyzed, and the extraction method of alfalfa mapping was established according to different spatial- and temporal-resolution remote sensing data. The results provided the following conclusions: (1) when using the wave peak and valley number of normalized difference vegetation index (NDVI) curves, in the study area, the number of wave peak needed to be greater than 2 and the number of wave valley needed to be greater than 1; (2) 91.6% of alfalfa sampling points were extracted by moderate resolution imaging spectroradiometer (MODIS) data using the wave peak and valley method, and 5.0% of oats sampling points were extracted as alfalfa, while no other vegetation types met these conditions; (3) 85.3% of alfalfa sampling points were identified from Sentinel-2 multispectral instrument (MSI) data using the wave peak and valley method; 6.0% of grassland vegetation and 8.7% of oats satisfied the conditions, while other vegetation types did not satisfy this rule; and (4) the temporal phase selection was very important for alfalfa extraction using single-time phase remote sensing images; alfalfa was easily separated from other vegetation at the pre−wintering stage and was more difficult to separate at the spring regreening stage due to the variability in the alfalfa overwintering rate; the overall classification accuracy was 92.9% with the supervised classification method using support vector machine (SVM) at the pre-wintering stage. These findings provide a promising approach to alfalfa mapping using different remote sensing data. [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]

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]

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]

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]

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]

As a unique ecosystem with multiple ecological functions but high fragility, grassland in arid areas is very vulnerable to changes in the natural environment or human activities, resulting in various ecological and environmental problems. In order to study the degree and spatial extent of the influence of climatic conditions and human activities, especially mining activities, on grasslands in arid regions, we used remote sensing data to monitor the vegetation of the Xilin Gol grassland over a long period. The significant greening and browning areas of Xilin Gol grassland vegetation from 2000 to 2020 were extracted by a time series analysis. At the same time, the correlation analysis method was used to obtain the response of the Xilin Gol grassland vegetation to climatic factors and social and economic factors. In addition, we propose a new method based on buffer analysis and correlation analysis to calculate the influence range of vegetation degradation due to mining. We used this method to determine the influence range of vegetation degradation in the main mining area of the Xilin Gol grassland. The results showed that the vegetation condition of the Xilin Gol grassland were slightly improved from 2000 to 2020. Its vegetation was significantly affected by precipitation, and more than 50% of the area's vegetation changes were highly correlated with precipitation changes. However, the area with the most serious vegetation degradation was mainly affected by human factors, and this part accounted for about 0.13% of the total area. In the form of direct damage and indirect effects (pulling population and economic growth to expand built-up areas), coal mining has become the main driving factor in the most significant areas of vegetation damage in the study area. Vegetation coverage in areas with significant greening and significant browning was highly correlated with economic factors, indicating that the vegetation changes were significantly affected by economic development. This study can reflect the vegetation changes and main driving factors in the overall and key areas of the Xilin Gol League and is a meaningful reference for the local balance of economic development and environmental protection. [ABSTRACT FROM AUTHOR]

The quantitative and qualitative assessment of post-disaster vegetation damage and recovery in the core area of the Wenchuan earthquake is of great significance for the restoration and reconstruction of natural ecosystems and the construction of human settlements in China. This study used time series analysis to determine the time of MODIS data and used the data to study the vegetation damage and restoration in the core area of the Wenchuan earthquake. The determined MODIS images were used to quantitatively analyze a series of vegetation damage changes and the vegetation recovery rate in the core area of the Wenchuan earthquake before and after the earthquake. By applying the topographic factors, we analyzed the spatial and temporal characteristics of the dynamic changes of vegetation damage and the recovery rate in the disaster area. The results show that the study area's vegetation damage was correlated to topographic factors and distance from towns. Besides, the overall vegetation restoration after the disaster was relatively optimistic. In some areas, the vegetation restoration level even exceeded the vegetation coverage level before the disaster. The recovery study of MODIS-NDVI showed a specific lag delay effect on the image of vegetation cover. The vegetation damage and the recovery rate of vegetation cover were significantly correlated with the distance from towns and the topographic factor. Overall, the results contribute to the theoretical support for the damage and recovery of vegetation in the core area affected by the earthquake. [ABSTRACT FROM AUTHOR]