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In the face of excessive urban growth, urban green spaces face the challenge of efficiently providing ecosystem and environmental services benefits. While public green spaces (PGS) stand out for their different environmental and social benefits, their efficiency depends on the vegetated cover, which can be evaluated in relation to the area, type of polygon, and degree of equipment. In this study, the effective green cover (EGC) assessed from a geographic information system, and the level of equipment–infrastructure were evaluated in different green spaces in the metropolitan area of San Luis Potosí, Mexico. The PGS categories included park with hydrological potential, urban park, linear park, neighborhood park, local garden, residual green space, and sports area. In our results, the urban park and the park with hydrological potential indicated 69.5 and 79.5% of EGC, respectively, and a value of 0.3 of Normalized Difference Vegetation Index (NDVI). Specifically, only in urban park, the land surface temperature (LST) decreased with the increase in the NDVI. The total green coverage of PGS was 6.7 m2 and the EGC was 5.8 m2, which is largely due to the large-sized parks. Furthermore, the provision of parks is insufficient compared to other metropolitan areas on national and international scales. In the spaces with the highest score of equipment, the outstanding elements were urban furniture, children's playgrounds, and exercise areas, which varied according to the type of green space, indicating a differentiated social use. This study suggests that EGC can be a parameter to assess the green proportion of PGS in cities. Integrating this variable in PGS planning and design can enhance ecosystem services provision in metropolitan areas. [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]

Flooded forests are rare and highly dynamic ecosystems, yet they can store a significant amount of carbon because of their ability to produce biomass rapidly. Estimation and mapping of the carbon that is stored in flooded forests are challenging tasks through the use of optical remote sensing because these ecosystems are often located in moist regions where clouds can interfere with data acquisition and image interpretation. This study models the aboveground carbon (AGC) stocks of a flooded forest in Turkish Thrace with synthetic aperture radar (SAR) data, which are less affected by weather and illumination conditions compared to optical imagery. Forest management plan data, including inventory records of 229 sample plots, a detailed forest cover map, and stand tables of the 2.119-ha Igneada Longoz Forest, were used to calculate AGC and to develop spatially explicit models based on ALOS/PALSAR-2 (Advanced Land Observing Satellite/Phased Array L-band Synthetic Aperture Radar) and Landsat-8 images. The results indicated that the horizontally transmitted and horizontally received (HH) and cross-polarization ratio (CPR) bands of ALOS/PALSAR were the most influential variables in the linear and nonlinear regression models. The models did not include any variables from either radar- or optical-based vegetation indices. While the estimation accuracies of the two models were similar (root mean square percentage error ≈ 26%), the linear model yielded negative estimations in several land cover classes (e.g., dune, forest opening, degraded forest). AGC stock was estimated and mapped using the nonlinear model in these cases. The density map revealed that Igneada Longoz Forest stored 279,258.9 t AGC, with a mean and standard deviation of 124 ± 115.4 t C ha-1. AGC density varied significantly depending on stand types and management units across the forest, and carbon hotspots accumulated in the northern and southern sites of the study area, primarily composed of ash and alder seed stands. The models and maps that this study developed are expected to help in the rapid and cost-effective assessment of AGC stored in flooded forest ecosystems across the temperate climate zone.

Abstract 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.

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]

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]

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]

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]

For the needs of precision agriculture, it is important to create a database on the trends of changes in vegetation indices. The correct interpretation of the data for a specific region is useful for reading and planning subsequent treatments. The research was conducted with a small UAV equipped with a NIR camera in the period 2019-2022, in three fields in southern Dobrudja. The aim is to track the dynamics of NDVI changes in wheat. To create a database on the trends of change in NDVI in the specific agrarian climatic conditions of southern Dobrudja. The dynamics of the index during the period of extreme drought 2019-2020 have been tracked. Maximum values are recorded in the spindle phase - grading at the beginning of May. Returning frosts at the beginning of March lower the value sharply. The dynamics of NDVI during the phenological development of wheat was tracked, as a maximum of 0.5 was reached during the grading period. Humidity analysis gives a direct link to NDVI change trends with a week lag of the changes that occurred. [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]

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]

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]

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]

The annual frequency of ground surface freeze–thaw (AFGSFT) on the Qinghai-Xizang Plateau (QXP) is one of the most prominent features of the high plateau ground surface processes. Seasonal freezing and thawing of the ground surface led to changes, and sometimes anomalies, in the energy balance between the ground surface and the atmosphere, thereby impacting the ecological environment. However, the relationship between AFGSFT and normalized difference vegetation index (NDVI), as major influencing factors of near-ground surface hydrothermal processes, has not been well elucidated. Based on meteorological observation data from 1982 to 2020, National Oceanic and Atmospheric Administration Climate Data Record (NOAA CDR) NDVI data, and some auxiliary data, this study employs trend analysis, GeoDetector, and correlation analysis to explore the impact of NDVI on AFGSFT. The findings indicate that AFGSFT on the QXP has gradually decreased, while NDVI has generally shown an upward trend. NDVI exerts a strong controlling effect on AFGSFT changes. Specifically, as AFGSFT decreases, NDVI tends to increase, but the increasing NDVI gradually inhibits the downward trend of AFGSFT. Thus, the relationship between NDVI and AFGSFT trend is not merely one of amplification or inhibition but rather exhibits a more complex nonlinear relationship. Moreover, the changes in AFGSFT and NDVI in grassland areas are greater than those in other land cover types. This may suggest that grassland regions are experiencing a more rapid climate response and ground surface processes. These findings contribute to a better understanding of the ground surface characteristics of the high plateau and provide data support for formulating scientific ecological protection and climate adaptation strategies.

The rapid urbanization in Sekondi-Takoradi, Ghana has significantly transformed the land cover, resulting in the proliferation of impervious surfaces and a decline in vegetation. However, the influence of this urban growth on the development of urban heat islands (UHIs) in the metropolis remains understudied. This study aimed to fill this research gap by employing Landsat images to explore the influence of urban growth on urban heat islands in the metropolis from 1991 to 2023. The supervised random forest technique was utilized to map the land cover changes. Furthermore, the computed normalized difference built-up index (NDBI), normalized difference vegetation index (NDVI), land surface temperature (LST), and urban thermal field variance index (UTFVI) were used to analyze the influence of urban expansion on UHIs. The findings revealed a 63.07 km2 increase in built-up areas and a 60.99 km2 decrease in vegetation cover during the study period. This dramatic land use change led to a 3.1°C rise in mean LST and a 19.38 km2 expansion of areas affected by the UHI effect. The UTFVI analysis further indicated a 33.63 km2 increase in the worst ecological zone due to the temperature rise. Statistical analysis between LST, NDVI, and NDBI revealed significant variability in explaining the intensity of LST and UHI in the metropolis over the study period. The study equips city authorities and planners with the fundamental knowledge needed to prepare a sustainable development plan that alleviates adverse effects of urban growth and elevated temperature-related issues. Also, the findings contribute to the global efforts in promoting more livable and climate-resilient urban environments.

Effects of climate change in semi-arid areas occur in drought events, which affect aquifers whose recharge depends essentially on precipitation. The objective of this study is to evaluate the relationship between depth to groundwater (DTW), precipitation, Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST), in the alluvial aquifer of Mostaganem Plateau, Algeria over 2000, 2005, 2010-2011 and 2014-2015. This is caried out through an adaptive methodology, using remote sensing, Geographic Information Systems (GIS), and statistical analysis: correlation analysis and Multiple Linear Regression (MLR). The results indicate a 62 mm decline in precipitation from 2000 to 2015, inducing shifts in spatial patterns. This resulted in an increase of DTW (4 m to 10 m). The strong negative correlation between decreased precipitation and increased DTW, supported by an R2 value of -0.80, is evident. Moreover, NDVI and LST values increased notably by 0.034 and 3.38°C, respectively. The relationship between DTW, NDVI, and LST showed a diminishing negative correlation. The MLR reaffirmed the influence of precipitation and highlighted the impact of human activity on DTW and drought indicators effectiveness. High NDVI values indicated intensive groundwater pumping, while elevated LST contributed to DTW decrease due to increased evaporation rates caused by changes in crop types resulting from human actions. This study contributes to the understanding of the dynamic interactions between DTW, precipitation, and anthropogenic activities and gives insight to decision makers regarding irrigation strategies.

The change in land cover associated with urbanization modifies the microclimate, leading to urban heat islands. As cities grow rapidly, many areas crucial for biodiversity are damaged day by day, and this negatively affects the urban ecosystem services (ES). This research examines the macroform change in parallel with the population increase over the years in the 5 central districts of the Province of Antalya (Konyaaltı, Döşemealtı, Kepez, Muratpaşa and Aksu), which were selected as the study area. In this context, Land Surface Temperature (LST) change data and the Normalized Difference Vegetation Index (NDVI) between 2007–2022 were analyzed and ES effects were revealed. The relationship between LST and NDVI was analyzed by correlating the LST and NDVI with the ES provided from the area through LULC data. While a high negative correlation was observed between the LST and NDVI dataset, the increase in residential, industrial and bare areas with increasing population between the specified years and the decrease in vegetation cover were established as the main parameter here. As a result, although the population growth in urban areas continues, the increase in ES and green infrastructure elements in urban settlements will lead to a decrease in LST values.

In the face of excessive urban growth, urban green spaces face the challenge of efficiently providing ecosystem and environmental services benefits. While public green spaces (PGS) stand out for their different environmental and social benefits, their efficiency depends on the vegetated cover, which can be evaluated in relation to the area, type of polygon, and degree of equipment. In this study, the effective green cover (EGC) assessed from a geographic information system, and the level of equipment–infrastructure were evaluated in different green spaces in the metropolitan area of San Luis Potosí, Mexico. The PGS categories included park with hydrological potential, urban park, linear park, neighborhood park, local garden, residual green space, and sports area. In our results, the urban park and the park with hydrological potential indicated 69.5 and 79.5% of EGC, respectively, and a value of 0.3 of Normalized Difference Vegetation Index (NDVI). Specifically, only in urban park, the land surface temperature (LST) decreased with the increase in the NDVI. The total green coverage of PGS was 6.7 m2 and the EGC was 5.8 m2, which is largely due to the large-sized parks. Furthermore, the provision of parks is insufficient compared to other metropolitan areas on national and international scales. In the spaces with the highest score of equipment, the outstanding elements were urban furniture, children’s playgrounds, and exercise areas, which varied according to the type of green space, indicating a differentiated social use. This study suggests that EGC can be a parameter to assess the green proportion of PGS in cities. Integrating this variable in PGS planning and design can enhance ecosystem services provision in metropolitan areas.

BACKGROUND AND OBJECTIVES: Mangroves play a crucial role in mitigating climate change by absorbing carbon stocks. However, there is a lack of information on mangrove distribution and their carbon absorption abilities. Therefore, this study aimed to bridge this gap by gathering data on the ability of mangrove forest areas to absorb carbon stocks. Specifically, this study aims to assess the carbon absorption potential of the Lantebung mangrove ecosystem through field surveys, allometric calculations, and unmanned aerial vehicle imagery.METHODS: The methodology employed in this study consisted of field surveys, allometric calculations, and multispectral aerial imagery processing along the coastal of Makassar City, South Sulawesi, within the Lantebung mangrove ecosystem. Field surveys were conducted to determine the species composition of each mangrove stand and measure their diameter at breast height. The allometric formula was then used to calculate mangrove biomass, which was subsequently converted into carbon stock values. Aerial imagery was processed using the normalized difference vegetation index, followed by a regression analysis between normalized difference vegetation index and carbon stock values to obtain a carbon stock estimation model.FINDINGS: The results of the analysis of red-green-blue aerial imagery from the multispectral unmanned aerial vehicle has provided valuable insights into the extent of mangrove vegetation cover in the Lantebung mangrove forest area, revealing it to be 14.18 hectares. The normalized difference vegetation index results indicated that mangrove objects fall within a value range of 0.21–1, categorized into three density classes: high-, medium-, and low-density mangroves. The field surveys confirmed the presence of three types of mangroves in Lantebung Makassar, namely Rhizophora apiculata, Rhizophora mucronata, and Avicennia sp. The regression analysis conducted to assess the relationship between the normalized difference vegetation index value and carbon stocks yielded the equation model carbon stock = 474.61, vegetation Index value + 17.238, with a linear regression value of 0.7945. The carbon stock values for low-density class mangrove areas were predicted to range between 17.24 and 288.64 tons carbon per hectare, medium-density mangroves' carbon stocks to be between 126.04 and 391.14 tons carbon per hectare, and high-density mangrove areas' carbon stocks to range from 258.04 to 491.85 tons carbon per hectare.CONCLUSION: The utilization of drones as a technique for monitoring carbon stocks has offered significant benefits. Drones equipped with multispectral sensors enable the collection of precise and comprehensive data on vegetation and elevation in many ecological systems. The survey and subsequent analysis highlighted the wide variation in the density of mangrove forests in the Lantebung mangrove ecosystem. This study demonstrated a strong correlation between the normalized difference vegetation index extracted using unmanned aerial vehicle and mangrove carbon levels obtained from actual field measurements.

Abstract African pastoralists suffer recurrent droughts that cause high livestock mortality and vulnerability to climate change. The index-based livestock insurance (IBLI) program offers protection against drought impacts. However, the current IBLI design relying on the normalized difference vegetation index (NDVI) may pose limitation because it does not consider the mixed composition of rangelands (including herbaceous and woody plants) and the diverse feeding habits of grazers and browsers. To enhance IBLI, we assessed the efficacy of utilizing distinct browse and grazing forage estimates from woody LAI (LAIW) and herbaceous LAI (LAIH), respectively, derived from aggregate leaf area index (LAIA), as an alternative to NDVI for refined IBLI design. Using historical livestock mortality data from northern Kenya as reference ground dataset, our analysis compared two competing models for (1) aggregate forage estimates including sub-models for NDVI, LAI (LAIA); and (2) partitioned biomass model (LAIP) comprising LAIH and LAIW. By integrating forage estimates with ancillary environmental variables, we found that LAIP, with separate forage estimates, outperformed the aggregate models. For total livestock mortality, LAIP yielded the lowest RMSE (5.9 TLUs) and higher R2 (0.83), surpassing NDVI and LAIA models RMSE (9.3 TLUs) and R2 (0.6). A similar pattern was observed for species-specific livestock mortality. The influence of environmental variables across the models varied, depending on level of mortality aggregation or separation. Overall, forage availability was consistently the most influential variable, with species-specific models showing the different forage preferences in various animal types. These results suggest that deriving distinct browse and grazing forage estimates from LAIP has the potential to reduce basis risk by enhancing IBLI index accuracy.

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]

The Moderate Solution Imaging Spectroradiometer (MODIS) is a multispectral sensor that has been actively researched in various fields using diverse land and atmospheric products. MODIS was first launched over 20 years ago, and the demand for novel sensors that can produce data comparable to that obtained using MODIS has continuously increased. In this study, land products obtained using the visible infrared imaging radiometer suite (VIIRS) of the Suomi National Polar-orbiting Partnership satellite launched in 2011 were introduced, including land surface temperature and vegetation indices such as the normalized difference vegetation index and enhanced vegetation index. These land products were compared with existing data obtained using MODIS to verify their local applicability in South Korea. Based on spatiotemporal monitoring of an extreme drought period in South Korea and the application of VIIRS land products, our results indicate that VIIRS can effectively replace MODIS multispectral sensors for agricultural drought monitoring. [ABSTRACT FROM AUTHOR]

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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]

African pastoralists suffer recurrent droughts that cause high livestock mortality and vulnerability to climate change. The index-based livestock insurance (IBLI) program offers protection against drought impacts. However, the current IBLI design relying on the normalized difference vegetation index (NDVI) may pose limitation because it does not consider the mixed composition of rangelands (including herbaceous and woody plants) and the diverse feeding habits of grazers and browsers. To enhance IBLI, we assessed the efficacy of utilizing distinct browse and grazing forage estimates from woody LAI (LAIW) and herbaceous LAI (LAIH), respectively, derived from aggregate leaf area index (LAIA), as an alternative to NDVI for refined IBLI design. Using historical livestock mortality data from northern Kenya as reference ground dataset, our analysis compared two competing models for (1) aggregate forage estimates including sub-models for NDVI, LAI (LAIA); and (2) partitioned biomass model (LAIP) comprising LAIH and LAIW. By integrating forage estimates with ancillary environmental variables, we found that LAIP, with separate forage estimates, outperformed the aggregate models. For total livestock mortality, LAIP yielded the lowest RMSE (5.9 TLUs) and higher R2 (0.83), surpassing NDVI and LAIA models RMSE (9.3 TLUs) and R2 (0.6). A similar pattern was observed for species-specific livestock mortality. The influence of environmental variables across the models varied, depending on level of mortality aggregation or separation. Overall, forage availability was consistently the most influential variable, with species-specific models showing the different forage preferences in various animal types. These results suggest that deriving distinct browse and grazing forage estimates from LAIP has the potential to reduce basis risk by enhancing IBLI index accuracy. [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]

Vegetation, as a direct carrier of acid deposition, is often disturbed by climate change and environmental pollution, with multiple effects on its growth and distribution. However, the patterns of acid deposition and meteorological factors on vegetation growth in acid rain regions remain unclear. Therefore, we quantified the relative contributions of acid deposition and meteorological factors to develop a vegetation NDVI in Shaanxi, Chongqing, Fujian, and Guangdong Provinces during 2001–2021. Additionally, we employed RF and geographically and temporally weighted regression models. A RF model identified the main drivers affecting the NDVI, and the geographically and temporally weighted regression model further revealed the spatial and temporal heterogeneity of these factors. A geodetector was also applied to assess the combined effects of multifactor interactions of NDVI changes. It was found that temperature (TEMP), SO42−, NH4+, and precipitation (PREP) were the main drivers of vegetation cover changes in the acid rain area, with contributions of 16.06 %, 12.03 %, 10.37 % and 10.04 %, respectively, and the impacts on NDVI showed significant spatial–temporal heterogeneity. The negative effect of SO42− and the positive effect of NH4+ on vegetation growth varied with time and geographical location. effect on vegetation growth and the positive effect of NH4+ on vegetation growth showed complex dynamics depending on time and geographic location. More importantly, the interactions between SO42−, NH4+, temperature (TEMP) and precipitation (PREP) had a greater effect on NDVI than that of a single factor, suggesting that the factors did not act independently, but rather drove the changes of vegetation in the acid rain area together. This study illustrates the pattern of vegetation change in response to acid rain and confirms the interaction between acid deposition and meteorological factors influencing plant growth. Our results support the formulation of policy measures to mitigate the impact of acid rain on vegetation.

A profound grasp of the quantitative spatial heterogeneity and distribution of the soil physicochemical attributes is crucial in understanding agricultural landscapes for ensuring the provisioning of soil ecosystem services. However, the analysis of data from remote sensing, like NDVI, can be of help in analysing the capacity of the landscape to provide supporting ecosystem services such as primary productivity. The research investigated and addressed the dispersion of important soil physico-chemical attributes in agricultural lands of the temperate Himalayan region of India using a geostatistical method and combining normalized difference vegetation index (NDVI) time-series data and the regression Kriging method. A 206 soil samples were gathered and assessed for soil parameters like pH, EC, OC, and available N, P, K, Ca, and Mg from Kishtwar district of Jammu. The coefficient of variation (CV) for pH and electrical conductivity (EC) ranged notably from 8.75 % to 118.98 %, highlighting diverse soil characteristics critical for local management practices. Mean elevation averaged 2743.32 m (m), with a moderate NDVI of 0.15, indicating dynamics in vegetation cover. Soil pH ranged from intensely acidic to marginally alkaline, with varying EC levels. Seemingly high organic carbon (OC), nitrogen (N), and potassium (K) levels, accompanied by medium phosphorus (P), calcium (Ca), and magnesium (Mg) levels were found in the region. The study employed ordinary kriging (OK) to map the spatial distribution of soil parameters, utilizing mean square error (MSE), root mean square error (RMSE), and the Moran’s I index. Exponential models were the best fit models for OC, while spherical models were fit for pH, EC, N, P, and Ca. Mathematical models were best fit for K and Mg. Spatial analysis using spherical and exponential models revealed distinct distribution patterns for pH, N, P, Ca, and Mg. The results of the degree of spatial dependence from the semi-variogram analyses indicated a strong (0.06 %) to moderate (0.51 %) to weak (2.81 %) dependence. The interpolated maps showed a distinct gradient in elevation (1053–4413 m), OC (0.13–2.80 %), NDVI (−0.16–0.54), pH (4.80–8.00), EC (0.03–9.80 dS m−1), N (201.15–993.19 kg ha−1), P (3.00–96.00 kg ha−1), K (124.88–1110.71 kg ha−1), Ca (7.00–46.00 meq 100 g soil−1), and Mg (2.30–21.50 meq 100 g soil−1) at the regional scale, indicating a wide range of spatial soil heterogeneity. The heterogeneity maps of soil parameters generated by this research can be effectively used by land planners and farm managers at a regional scale for crop nutrient management to reduce soil contamination risk. These maps serve as baseline materials and effective tools for suitable land management strategies such as conservation-effective tillage, integrated nutrient management, and organic farming based on the spatial distribution of soil properties and they can significantly enhance the long-term ecological sustainability of agro-ecosystems’ management.

Spatial-temporal dynamics monitoring of Arctic vegetation structure (i.e. distribution range of tundra and forest) is of great significance for evaluating global warming effect. Currently, time-series monitoring of Arctic vegetation structure relies primarily on the Normalized Difference Vegetation Index (NDVI), which is derived from optical remote sensing images. However, because of factors such as the long revisit period of satellites and the impact of climate, optical observations are severely lacking in the Arctic region. This results in NDVI time-series data highly discontinuous and difficult to reflect actual variations in Arctic vegetation structure, and the traditional time-series reconstruction method would usually fail for severe missing conditions. Therefore, this study developed a Time Series Reconstruction method considering Periodic Trend (TSR-PT), which is specifically for alleviating the severe missing observation condition in the Arctic region. It can separate the phenological change and trend change of the incomplete time series NDVI, and borrow the information from the neighboring unchanged years for compensate of the missing observations in current years, based on the learned inter-annual and intra-annual correlation. We explore its usability in monitoring vegetation structure variation in Vorkuta region (transition zone of tundra and taiga in the Arctic Circle) based on MODIS data. It is found that the proposed TSR-PT is able to reconstruct NDVI with reasonable phenological feature even the missing rate reaches over 70%, which is usually falsely constructed by traditional filtering or fitting method, and suppress them by 0.038 in terms of RMSE; besides, we find that since 21-century, the Arctic trees have continued to increase and encroach the original tundra ecosystem, which caused a largely Arctic vegetation structural change, and we believe the proposed method would largely promote the Arctic vegetation research.

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]

Estimating the cover and management factor (C -factor) for Universal Soil Loss Equation (USLE) that varies spatially and temporally within a watershed is time-consuming and resource-intensive. The Normalized Difference Vegetation Index (NDVI) approach can offer a potential alternative for this process. The current study examines nine NDVI models to compare and evaluate their performance in estimating the C -factor values for an agricultural watershed in southwestern Ontario, Canada. Satellite imagery from 2013 to 2020 was used to analyze the models' similarities and differences on a detailed spatial and temporal scale. The results showed different C- factor values for each model, reflecting that they were developed for different geographical areas and purposes. While the Karaburun model differed from all other models on an annual basis, a detailed combined analysis of different spatial and temporal scales revealed that it was similar to other models. Seasonal analysis was found to be adequate for the current study, as it reduced the resources required and provided an overall view of the vegetation situation. However, a detailed monthly analysis may be necessary when investigating a specific season. The current analysis found that the summer months of June, July, and August have similar trends when comparing different models for different land uses and individual months, which aligns with the seasonal analysis. In conclusion, the current study highlights the importance of incorporating spatial and temporal scales in hydrological modeling and provides valuable insight into the applicability of different NDVI models for estimating the C- factor for southwestern Ontario watersheds. These findings can help inform future research and aid in developing accurate models for estimating soil erosion in this region. The results also emphasize that the NDVI approach has the potential for estimating the USLE C- factor and improving the estimation of soil erosion from agricultural watersheds by incorporating a variable C- factor over time and space. However, further research is needed to validate each model and determine which model best suits the study area. [ABSTRACT FROM AUTHOR]

Droughts are one of the most catastrophic natural disasters on the planet, impacting millions of people in various ways (e.g., food security, economic losses, and migration). The growing severity of droughts and their catastrophic effects on society in India's semi-arid regions necessitate better drought monitoring and assessment systems. Traditional mathematical methods like Standardized Precipitation Index (SPI), Palmer Drought Severity Index (PDSI), and Climate Moisture Index (CMI), etc., are used to analyze drought intensity. In the present study, analysis of historical rainfall data from the Parbhani district (2000–2020) was conducted to identify patterns in normal, actual, and % deviation of actual rainfall from normal for two contrasting years (2020: +4.1 % deviation, 2015: −53.73 % deficit), with a focus on drought analysis using dry spell data from the Government senses. The satellite images for the years 2015 and 2020 were gathered at 16-day intervals, and remote sensing techniques were employed to calculate NDDI (normalized difference drought index) using NDVI (normalized difference vegetation index) and NDWI (normalized difference water index). The comparative analysis of NDVI and NDWI interpreted greater value during normal rainy conditions in 2020, whereas it was low in 2015 due to less rainfall and higher dry spells. NDDI reflects positively; it was observed that the mean area under the Mild drought class in 2015 (90.9 %) was higher than in 2020 (84.9 %). Variable rainfall distribution and dry spell patterns caused severe drought in 2015, according to vegetation indices. The study utilized remote sensing to investigate drought and corroborated its findings with India Meteorological Department (IMD) rainfall and dry spell data. VIs can therefore be utilized as an independent indicator that complements conventional techniques.

This study presents a novel classification model, the Normalized Difference Vegetation Index (NDVI), degree of polarization (DOP), texture classification model (NDTCM), for both regional and global scales, utilizing a synergistic approach that combines the NDVI from optical data with the DOP and its associated texture from synthetic aperture radar data. This integration effectively enhances land use and land cover (LULC) mapping. Specifically, Multi-date Moderate Resolution Imaging Spectroradiometer/Landsat images for NDVI extraction and dual polarization Phased-Array L-band Synthetic Aperture Radar data for DOP are employed in this study. This integration enables the NDTCM to effectively classify land cover into five categories: forest, shrubland, urban, cultivated land, and bare surface. Applied to Mediterranean land cover mapping, the NDTCM achieved high accuracy, with rates of 93.3% for forests, 57.5% for shrublands, 64.4% for urban areas, 76.8% for cultivated lands, and 92.8% for bare surfaces. Compared with global land-cover models, such as GlobCover, the NDTCM showed superior performance in forest and shrubland classification, exceeding GlobCover's accuracy of 84.3% for forests and 35.4% for shrublands, in this study case. The contribution of each data source to the classification results was significant. NDVI data were instrumental in identifying vegetative cover. The DOP and texture information enriched the model's capability to discern land cover types by providing insights into the physical structure and heterogeneity of the surfaces, critical for distinguishing between different land covers, such as forest and shrubland. This comprehensive integration demonstrates the NDTCM's potential as a robust framework for future advancements in LULC mapping and environmental studies.

Accurate prediction of vegetation indices is useful for helping maintain vegetation stability, sustaining food production, and reducing socioeconomic losses. The traditional convolutional long short-term memory (ConvLSTM) model for vegetation prediction ignores the spatial aggregation characteristics of the normalized difference vegetation index (NDVI) itself and the global dependence information in space. In this study, we propose a new NDVI prediction method, namely, the ConvLSTM with spatial autocorrelation and nonlocal attention module (ConvLSTM-SAC-NL), by combining the nonlocal attention module to capture long-range dependence and the spatial autocorrelation modeling based on the local Moran index to learn spatial dependence. The experimental results indicate that the ConvLSTM-SAC-NL model outperforms seven baseline forecasting models, with an R${}^{2}$ of 0.881 in monthly NDVI prediction in the Huangpi district of Wuhan city, relative to the R${}^{2}$ values of 0.758, 0.777, 0.741, 0.776, 0.804, 0.829, and 0.815 for random forest, support vector machine regression, long short-term memory, bidirectional long short-term memory, graph convolutional network, predictive recurrent neural network, and ConvLSTM models, respectively. Spatially, the prediction results of the ConvLSTM-SAC-NL model demonstrate improved accuracy over 91.49$\%$ of the study area when compared with ConvLTSM. Therefore, the proposed ConvLSTM-SAC-NL model could serve as an effective approach for short-term prediction of vegetation conditions at regional scales.

Accurate vegetation behavior forecasting is essential for understanding the dynamics of plant life in the context of climate change and other natural or human-induced disturbances. Recurrent neural network (RNN) deep learning (DL) models represent a modern approach to predict vegetation behavior with a high level of precision. In this article, we explore the potential of different DL and more traditional methods to forecast the normalized difference vegetation index (NDVI), which is directly related to the state of vegetation and its dynamics. A time-series dataset consisting of 70 NDVI images calculated from PlanetScope data from April 2017 to January 2023 was used. Initially, all selected methods were evaluated and compared. From the six tested methods, simple RNN (SRNN) proved to be the most accurate method for predicting vegetation dynamics. The SRNN model results achieved a mean RMSE of 0.051 when compared to the actual 2022 NDVI values. The high accuracy was reflected in all five studied vegetation classes characterizing the selected Mediterranean test area. The SRNN method performs very well in most months, except in autumn where it underestimates NDVI values. To get a thorough insight into the results, we also compared them to the Sentinel-2 NDVI data and climate data consisting of temperature and precipitation values. It was found that most of the prediction differences were due to the irregular variations in meteorological conditions during the year analyzed. The predictive capabilities of RNNs are an effective tool for forecasting vegetation dynamics but can be further improved by incorporating climate data into the prediction process.

Rapid urbanization and industrialization in Lahore and Faisalabad have intensified air pollution issues, influencing nitrogen dioxide (NO2) concentrations, land surface temperature (LST), and vegetation. The study aims to comprehensively assess changes in NO2, LST, and vegetation induced by industrialization, focusing on seasonal variations from 2019 to 2022. The study evaluates NO2 concentrations vegetation health using indices normalized difference vegetation index, enhanced vegetation index, atmospherically resistant vegetation index, and LST variations. The analysis reveals a notable increase in NO2 during both summer and winter, with approximately 0.021 (×103 mol/m2) and 0.03 (×103 mol/m2) rises observed in Lahore. In comparison, Faisalabad experienced more modest increases of around 0.0034 (×103 mol/m2) and 0.007 (×103 mol/m2) in the respective seasons. Simultaneously, vegetation indices decline in both cities, indicating substantial vegetation health deterioration. Moreover, a notable upward trend in LST occurred, with Lahore experiencing an increase of approximately 1.59 °C in summer and 0.92 °C in winter. Faisalabad also showed rises of around 1.64 and 0.54 °C in the corresponding seasons. Pearson correlation analysis highlights a robust negative correlation between NO2 and vegetation indices, underlining the impact of declining vegetation on air quality. A positive correlation between NO2 and LST indicates the interconnected nature of rising temperatures and air pollution. The findings emphasize the need for environmental regulations in Lahore and Faisalabad. Addressing rising NO2 levels and temperatures is critical for policymakers and urban planners. These insights contribute to the sustainable development goal 11, fostering strategies for sustainable cities and communities to combat pressing environmental challenges in these urban areas.

Multispectral reflectance—signals reflected from the Earth's surface across different wavelengths—is a primary data source for most remote sensing applications. However, obtaining complete cloud-free multispectral reflectance time series during the vegetation growing season remains challenging due to cloud contamination and limitations of existing reconstruction methods. To address the challenge, this study proposed a novel normalized difference vegetation index (NDVI)-guided bi-directional recurrent reconstruction model for multispectral reflectance time series (referred to as “NB-Re3”), which aimed to reconstruct dense time series of reflectance images by exploiting the dependence of NDVI on multispectral reflectance. NB-Re3 utilizes a temporal convolutional network to capture the temporal trends in the NDVI data and a bidirectional long short-term memory to integrate the temporal features of the NDVI with the cloud-free reflectance data. The architecture establishes a robust dynamic NDVI-reflectance relationship while capturing temporal dependencies and multispectral autocorrelations of multiple spectral bands. We compared the performance of NB-Re3 with four representative methods (MNSPI, HANTS, STAIR, and U-TILSE) in reconstructing multispectral reflectance time series, ranging from the visible bands to near-infrared and short-wave infrared bands, at two challenging sites: the irrigated area of Colleambally, Australia, and the cultivated area of Rikaze on the Southern Tibetan Plateau, China. The result showed that NB-Re3 kept superiority with the lowest root-mean-square error values and highest correlation coefficients values. The effectiveness of integrating high-quality NDVI time series and using multispectral autocorrelation to improve reflectance time-series reconstruction was further confirmed by the ablation experiments. It is concluded that NB-Re3 shows promise for generating long-term cloud-free reflectance time-series products tailored for ecological and agricultural applications.

The incompleteness of the normalized difference vegetation index (NDVI) time series (TS) restricts its expanded applications in key domains. Although spatio-temporal hybrid methods show promise in TS reconstruction, reliance on auxiliary data in most existing approaches introduces errors and increases workload. Furthermore, NDVI values marked as contaminated in the quality assessment (QA) data are underutilized. Ultimately, when utilizing spatial information, most methods are ineffective for the representation of land-use changes. Considering these issues, we propose a local peak Savitzky–Golay (LPSG) method for spatio-temporal reconstruction of Landsat NDVI TS. First, we construct a local peak neighborhood weighted interpolation (LPNWI) method that fully utilizes all original values to fill gaps. Second, we design a slope change decision tree (SC-DT) method for identifying residual noise, thereby mitigating the impact of QA errors on reconstruction results. Third, multidimensional calibration with weighted spatial reference (MDC-WSR) method is proposed to enhance utilization of spatial information by improving traditional correlation coefficient calculations and generating a multiyear spatial reference, which effectively reflects land-use changes. Experiments on Landsat NDVI TS data in the Qinghai–Tibet Plateau (2013–2022) show that: 1) LPSG outperforms other methods in mitigating the impact of QA errors, preserving TS peaks and details, and maintaining spatial continuity; 2) LPSG exhibits superior performance, with average RMSE reductions ranging from 0.00018 to 0.00750 compared to other methods under both correct and incorrect QA; and 3) LPSG demonstrates good robustness under various gap conditions and effectively restores TS of pixels affected by land-use changes.