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Abstract Much research has been invested in infrared temperature (IRT)-based methods for cotton (Gossypium hirsutism L.) water stress detection using in-field sensors, but adoption of these is low, perhaps due to logistical challenges. Alternatively, the Water Deficit Index (WDI) was developed for crop water stress assessment using remote sensors not embedded in the canopy. The objective of this research was to evaluate the performance of a sensor package—including modern IRT and normalized difference vegetation index (NDVI) sensors facing downward at 45˚, and a mini weather station—attached unintrusively to a center pivot irrigation system for detecting cotton water stress using WDI. Sensor packages were evaluated in a two-year field study that included four irrigation treatments (0, 30, 60, and 90% ET replacement) and in two production cotton fields. Overall, the tested system was effective at distinguishing crop water stress among irrigation rates. Comparison of the results to a ground-based station and simulations indicated that WDI overestimated water stress at the highest irrigation rate, but performed well otherwise. Accuracy of the system could be improved by measuring canopy coverage (Fc) from the same vantage point as the IRT and NDVI sensors (from the pivot, downward at a 45˚ angle).

Abstract Rapid replacement of vegetated land with impermeable land (built-up areas) is a major factor in the increase in Land Surface Temperature (LST), while increased LST worsens the temperature in cities and creates the Surface Urban Heat Island (SUHI) effect. The study aims to measure vegetation loss and Land Surface Temperature of the Gazipur district between 2000 and 2020 and explore the relationship among Normalized Difference Vegetation Index (NDVI), LST, and Urban Thermal Field Variance Index (UTFVI). The Landsat TM/OLI images with minimum cloud coverage have been used to derive different indices. The mean NDVI values are 0.21, 0.16, and 0.22 in 2000, 2010, and 2020 respectively which indicates a general improvement in the health of the vegetation. Besides, the highest LST values throughout 20 years, represent a general increasing trend. As a consequence, different land covers have experienced fluctuations in mean temperature. The result shows that the mean temperature of bare land, buildup, vegetation, and waterbody has increased by 4.77, 2.01, 2.25, and 2.23 °C respectively from 2000 to 2020. The strongest SUHI zone’s area grew by about 28% between 2000 and 2020. Additionally, the highest index value of UTFVI was 0.39 in 2000 and grew to 0.43 in 2010. It changed to 0.49 in 2020, or ten years later. Thus, the SUHI effect’s increasing intensity is visible. Also, regression analysis has been used to explore the correlation between the derived indices. Stakeholders from different sectors like urban planners and policymakers may take insights from this study to work to promote greenery for a healthy urban environment.

As a result of the Russian armed aggression, Ukraine has lost almost a third of its accumulated fresh water reserves worth more than USD18 billion. It has caused a loss of access to quality drinking water for 6 million people in Ukraine and more that 13 million people have a limited access to water for satisfying sanitary and hygienic needs. The situation is complicated due to the destruction of the Kakhovka hydroelectric power station dam which has led to a large-scale man-made disaster, severe negative environmental and socioeconomic consequences, the beginning of natural-climatic transformations of the drained water reservoir. The article presents comprehensive research of the state of the Kakhovka Reservoir functioning before and after the destruction of the hydroelectric power station dam on the basis of the actual facts and the results of decoded satellite imagery of Sentinel 2, Sentinel-3 and Landsat 8–9. It was found that the water reservoir drainage has caused disruption of microclimatic conditions, an increase in the air temperature by 2.0°С and more, accelerated evapotranspiration by 1.41–2.04 times, exacerbation of water scarcity in 58.2% of the territory of the drained water area and in the adjacent territories. About 52.0% of the territory of the drained water reservoir has stressful conditions of natural-climatic functioning. The obtained results confirm that the formed aboveground plant biomass of bottom landscapes is not capable of creating appropriate microclimatic conditions which existed in the water area of the Kakhovka Reservoir.

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

ABSTRACTChange in land use land cover (LULC) is driven by human activities and drives changes that limit the accessibility of services and products for livestock and humans. This study aimed to develop the spatio-temporal changes in LULC, vegetation cover, and moisture index using multi-temporal satellite data in Southern Punjab, Pakistan. Analysis of satellite data was accomplished using ArcGIS 10.4 software. Based on ground-truthing, the supervised classification technique (maximum likelihood algorithm) was used to achieve the LULC classification. The LULC change analysis revealed that vegetation area converted to the build-up area by 7.17 % and bare soil converted to urban areas by 1.68 % from 2000 to 2021. Average NDVI values were calculated at 0.23, 0.17, 0.19, and 0.14 for 2000, 2007, 2014, and 2021, respectively. In the study area, average NDMI values were observed at 0.28, 0.25, 0.2, and 0.15 for 2000, 2007, 2014, and 2021, respectively. Based on our study, the general trend in Southern Punjab is a decrease in vegetation cover, moisture index, and forest area due to an increase in build-up areas. Assessments of LULC and NDMI changes, as well as estimates of the effect on the environment, are necessary for many policy decisions and planning.

Having an additional tool for swiftly determining the extent of flood damage to crops with confidence is beneficial. This study focuses on estimating rice crop damage caused by flooding in Candaba, Pampanga, using open-source satellite data. By analyzing the correlation between Normalized Difference Vegetation Index (NDVI) measurements from unmanned aerial vehicles (UAVs) and Sentinel-2 (S2) satellite data, a cost-effective and time-efficient alternative for agricultural monitoring is explored. This study comprises two stages: establishing a correlation between clear sky observations and NDVI measurements, and employing a combination of S2 NDVI and Synthetic Aperture Radar (SAR) NDVI to estimate crop damage. The integration of SAR and optical satellite data overcomes cloud cover challenges during typhoon events. The accuracy of standing crop estimation reached up to 99.2%, while crop damage estimation reached up to 99.7%. UAVs equipped with multispectral cameras prove effective for small-scale monitoring, while satellite imagery offers a valuable alternative for larger areas. The strong correlation between UAV and satellite-derived NDVI measurements highlights the significance of open-source satellite data in accurately estimating rice crop damage, providing a swift and reliable tool for assessing flood damage in agricultural monitoring.

Annual crop monitoring is a key parameter for managing agricultural strategies. Several studies have relied on remote sensing products such as the normalized difference vegetation index (NDVI) as a vegetation dynamic metric. However, the dependence of optical data on weather conditions limits its availability. In this study, we reconstruct the NDVI time series of wheat fields using the moving averages of the Sentinel-1 normalized VH/VV cross-polarization ratio (IN) and the interferometric coherence in VV polarization over wheat selected fields in a semiarid site in Tunisia during two seasons, from 2018 to 2020. The crop cycle is divided into two periods: before and after the heading phase, which occurs in approximately the middle of March. Due to the volume-scattering impact, the second phase is divided into the ripening and maturation phase (NDVI ≥0.4) and senescence phase (NDVI

ABSTRACTThis study was conducted to investigate the effect of farming and pasture area extensions on land use and land cover in the North Eastern Corridor of Ghana. Landsat 5 TM+ image period of 2000 and Landsat 8 TOA satellite for the periods 2013 and 2022 were used. All images were captured at approximately the same period to ensure the selected images had the same reflectance values. A supervised machine learning technique using the algorithm of the random forest classifier was employed for the preparation of the classification of thematic maps. The Markov chain model was used to examine the dynamics of land use and land cover (LULC) changes in the study area. Visual appraisal of the images indicated some level of notable changes across the various classes from 2000 to 2022. The trend of the various changes in percentage terms also supports this observation. The results reveal that there was an improvement in the vegetation cover from 2000 to 2013 as reflected in the maximum and median NDVI values of the classified images within the period. However, the results show a considerable decline in vegetation health from 2013 to 22. Based on these results, we recommend that a more in-depth analysis to identify other possible anthropogenic activities and factors, that may serve as significant underlying causes of these vegetation cover changes in the region. The Ministry of Food and Agriculture (MoFA) should train farmers to incorporate tree planting into their farming whilst avoiding deforestation and bush burning within the area.

Developing an efficient and sustainable precision irrigation strategy is crucial in contemporary agriculture. This study aimed to combine proximal and remote sensing techniques to show the benefits of using both monitoring methods, simultaneously assessing the water status and response of ‘Calatina’ olive under two distinct irrigation levels: full irrigation (FI), and drought stress (DS, -3 to -4 MPa). Stem water potential (Ψstem) and stomatal conductance (gs) were monitored weekly as reference indicators of plant water status. Crop water stress index (CWSI) and stomatal conductance index (Ig) were calculated through ground-based infrared thermography. Fruit gauges were used to monitor continuously fruit growth and data were converted in fruit daily weight fluctuations (ΔW) and relative growth rate (RGR). Normalized difference vegetation index (NDVI), normalized difference RedEdge index (NDRE), green normalized difference vegetation index (GNDVI), chlorophyll vegetation index (CVI), modified soil-adjusted vegetation index (MSAVI), water index (WI), normalized difference greenness index (NDGI) and green index (GI) were calculated from data collected by UAV-mounted multispectral camera. Data obtained from proximal sensing were correlated with both Ψstem and gs, while remote sensing data were correlated only with Ψstem. Regression analysis showed that both CWSI and Ig proved to be reliable indicators of Ψstem and gs. Of the two fruit growth parameters, ΔW exhibited a stronger relationship, primarily with Ψstem. Finally, NDVI, GNDVI, WI and NDRE emerged as the vegetation indices that correlated most strongly with Ψstem, achieving high R2 values. Combining proximal and remote sensing indices suggested two valid approaches: a more simplified one involving the use of CWSI and either NDVI or WI, and a more comprehensive one involving CWSI and ΔW as proximal indices, along with WI as a multispectral index. Further studies on combining proximal and remote sensing data will be necessary in order to find strategic combinations of sensors and establish intervention thresholds.

Wetlands provide resources, regulate the environment, and stabilize shorelines; however, they are among the most vulnerable ecosystems in the world. The classification of mangrove species allows the determination of the habitat of each species, thereby serving as a basis for determining protection solutions and planning plans for mangrove conservation and restoration according to each environmental condition. We used Phantom 4 multispectral unmanned aerial vehicles (UAVs) to collect data from wetland areas in the Dong Rui Commune, which is one of the most diverse and valuable wetland ecosystems in northern Vietnam. A tree-species classification map was constructed through a combination of the object-based image analysis method and spectral reflectance values of each plant species, and the characteristic distributions of mangrove plants, including Bruguiera gymnorrhiza, Rhizophora stylosa, and Kandelia obovata, were determined with an overall accuracy of 91.11 % and a kappa coefficient (K) of 0.87. The overall accuracy for Rhizophora stylosa was the highest (94.23 %), followed by Kandelia obovata (93.61 %) and Bruguiera gymnorrhiza (85.50 %). An experiment was conducted to map plant taxonomy in the same area based only on a graph of spectral reflectance values at five single-spectral bands, and normalized difference vegetation index values were constructed, resulting in an overall accuracy of 78.22 % and a K of 0.67. The constructed map is useful for classifying, monitoring, and evaluating the structure of each group of mangroves, thereby serving as a basis for determining the distribution of each mangrove species according to natural conditions and contributing to the formulation of policies for afforestation and mangrove conservation in Dong Rui commune.

Previous research on watershed assessments has primarily emphasized general hydrological or technical findings, neglecting the spatiotemporal analysis of land-use change and its influence on surface runoff. This study addresses this gap by using a geographic information system (GIS)-based method to analyze surface runoff variations due to land-use change on Tsushima Island, Japan. By leveraging spatiotemporal analysis within a GIS framework, the study interprets satellite imagery (Landsat 8 data, 2014–2024) to assess land use and watershed characteristics. This approach culminates in a comprehensive map depicting potential flood risk across the island. In conclusion, this study offers an advanced method for communicating potential flood risks to the public and policymakers, enabling proactive mitigation strategies for the coming decade.

Analyzing vegetation greenness considering climate and land cover changes is crucial for Bangladesh given the historically drier North-West and South-West regions of Bangladesh have shown prominent climatic and hydrological variations. Therefore, this study assessed the spatial and temporal variation of NDVI and its relationship with climate and land cover changes from 2000 to 2022 for these regions. In this study, Moran's I and Getis Ord Gi* were employed for spatial autocorrelation and Mann-Kendall, Sen's slope test along with Innovative Trend Analysis were deployed to identify temporal trends of NDVI. RMSE, MAE and R-squared values were assessed between computed and observed PET. Correlation of NDVI with climate variables were assessed through multivariate correlation analysis and correlation mapping. Additionally, Pearson product moment correlation was applied between different types of land cover and NDVI. Spatial autocorrelation outcomes showed that NDVI values have been clustered in distinct hotspots and cold-spots over the years. Temporal trend detection results indicate that NDVI values are rising significantly all over the regions. Multivariate correlation analysis identified no climate variable to be the limiting factor for NDVI changes. Similarly, the precipitation-NDVI correlation map displayed no significant correlation. Nonetheless, temperature-NDVI correlation map illustrated varying degrees of mostly moderate and strong positive correlations with distinct negative correlation results in the Sundarbans of South-West region. Land cover pattern analysis with NDVI showed a positive correlation to forest, cropland and vegetation area increasing and negative correlation to grassland and barren area decreasing. In this regard, Rangpur division exhibited stronger correlations than Rajshahi division in North-West. The findings indicate that NDVI changes in the regions are largely dependent on land cover changes in comparison to climate trends. This can instigate further research in other hydrological regions to explore the natural and man-made factors that can affect the greenery and vegetation density in specific regions.

Background: Canopy phenology, foliar biochemistry, and leaf lifespan vary according to season within and across species. Intrinsically, this variation should also determine changes in the spectral properties of foliar tissue. However, whether canopy phenology and leaf spectroscopy co-vary across species has seldom been tested in seasonal drylands. Questions and / or Hypotheses: We tested the covariation between growth rate, foliage phenology, and leaf spectral indices, expecting that species with high growth rates should have large variability in relative leaf number and foliar spectroscopy. Studied species / data description / Mathematical model: We partition the intra and interspecific variation in relative leaf number and spectroscopy within nine perennial chaparral shrub species. Study site and dates: Rancho El Mogor, Valle de Guadalupe, Baja California, during 2017 and 2018. Methods: Foliage phenology was quantified with an index of relative leaf number, foliar spectroscopy with three standard vegetation indices, and resource-use with stem growth and leaf functional traits. Results: Species with higher variability in foliage phenology (more deciduous species) had higher stem growth rates. The three vegetation indices varied significantly across species (ca. 20 % of explained variance), and across seasons within species (ca. 55 % of explained variance). The values of the three vegetation indices were correlated, and only NDVI was correlated with foliage phenology and stem growth. Conclusions: Our results show that variations in relative leaf number and spectroscopy in chaparral species are coupled with functional traits of species involved in resource exploitation strategies in a seasonally arid environment.

Abstract Anthropogenic and climate-related phenomena are among the main factors responsible for variations in vegetation structure and composition worldwide. However, studies that integrate the effects of human activities and climate variability in fragile tropical ecosystems, including the semi-arid Tigray region, are lacking. The objective of this study was to examine the effects of climate variability and environmental policy changes on the spatial distribution and pattern of vegetation cover in the semi-arid Tigray region of Ethiopia over the past four decades. We used satellite-based vegetation index (normalized difference vegetation index) and monthly rainfall data to analyze the relationship between vegetation cover and climatic variability. Residual analysis was also used to further disentangle the effects of climatic variability and environmental policy on vegetation cover. The regression analysis (r2 = 0.19) showed an insignificant causal relationship between vegetation dynamics and precipitation over the 41-years study period. This study also highlighted negative impact of the global rise in temperature on vegetation cover due to water stress caused by evapotranspiration. On the other hand, the residual analysis results (r = − 0.55, z-stat = − 11.58, p

Large carnivores such as the jaguar Panthera onca are particularly susceptible to population decline and local extinction as a result of habitat loss. Here we report on the long-term monitoring of a local jaguar population in a mixed land-use area in the eastern lowlands of Bolivia from March 2017 to December 2019. We recorded 15 jaguar individuals and four reproduction events (five offspring from three females), suggesting that our study area harbours a resident breeding population. Seven iterations of spatially explicit capture–recapture models provided density estimates of 1.32–3.57 jaguars per 100 km2. Jaguar capture rates were highest in forested areas, with few to no jaguar captures in pastures used for livestock. Massive deforestation after the survey period reduced the proportion of dense forest cover by 33%, shrinking the availability of suitable jaguar habitat and placing the resident jaguar population at risk. We use the jaguar as an indicator species to highlight the threat of habitat destruction in the Chiquitano region and we emphasize the importance of intact forest patches for jaguar conservation.

Glasswort (Salicornia herbacea) is an annual succulent plant that grows widely around intertidal zone of Gomishan Lagoon in eastern boarders of the Caspian Sea in Iran. Due to its medicinal, industrial, and economic values, tendency of its plantation is growing in recent years. One of the challenges to manage a glasswort farm is to know the appropriate date to harvest since glasswort seeds ripe quickly and seed fall happens considerably by little shake. One way of defining appropriate harvesting date can be found through correlating the amount of visible greenness of the plant, and it's ripening stage with NDVI values derived from remote sensing imageries . This experimental research was conducted to answer this problem by relating the changes in Glasswort visually estimated color classes and its amount of seedfall to the amount of Normalized difference Vegetation Index (NDVI) values that were obtained from Sentinel-2 remotely sensed imagery during the seedfall period of Glasswort community either in the field or in its natural habitat. The maximum NDVI values of 41 Sentinel-2 images during 2018-2020 within Glasswort phenological period were extracted and were correlated with the color class of the plant and the weight of seedfall in sample plots. Results showed a strong correlation between NDVI and brownness color class of the plant (R2=0.80) and a strong negative correlation with amount of its seed fall (R2=-0.83).

The primary objective of this study is to investigate the carbon footprint, resilience levels, and optimal landscape area ratios of various parks. Additionally, it explores the relationships between landscape element proportions (LEP), the normalized difference vegetation index (NDVI), resilience indicators (RI), and the carbon reduction benefits associated with carbon neutrality (CN). Six parks were assessed for resilience, NDVI, LEP, and CN values, with Pearson correlation analysis conducted. The results revealed that parks with or without waterbodies exhibited ideal LEP area ratios of 6.5:2:1.5 (Softscape:Waterbody:Hardscape) and 8.3:1.7 (Softscape:Hardscape), respectively. Enhanced Softscape and reduced Hardscape proportions in parks correlated with increased NDVI and CN. NDVI exhibited a positive correlation with Softscape percentage and a negative correlation with Hardscape percentage. Conversely, CN demonstrated a negative correlation with Hardscape percentage and a positive correlation with Softscape percentage. Suggesting Softscape should constitute over 65%, and Hardscape should be under 15% in parks with water bodies. Waterless parks are advised to maintain a Softscape ratio exceeding 83% and a Hardscape ratio below 17%. Finally, the study extended to assess the LEP of 22 additional parks, validating the suitability of the ideal LEP area ratio.

In this study, a stepwise multifactor vegetation regression analysis (SMVRA) approach was proposed to investigate the interaction of multiple climate factors on vegetative growth in the study area from 2000 to 2020. It was developed by integrating the stepwise linear regression method, Standardized Precipitation Evapotranspiration Index (SPEI), Normalized Difference Vegetation Index (NDVI), and Pearson correlation coefficient. SMVRA can be used to intuitively understand the interactive effects of multiple correlated factors (e.g., temperature, precipitation, potential evapotranspiration, and the drought index) upon vegetation. The results show that the resilience of vegetation in the BLR basin is influenced by the severity of drought. Annual changes in SPEI over the BLR basin show an increasing trend, with rates of 3.12 × 10−2. Precipitation and NDVI had a strong positive correlation (p < 0.05), found for 34.93% of the total pixels in the study area. In the BLR basin, vegetation growth is inhibited in the 4 years following a drought event. The area near 800 m is most sensitive to drought events. It provides a theoretical basis for future drought response and effective vegetation restoration in the region.

Green spaces play a critical role in enhancing the urban environment, improving livability, and providing essential ecosystem services. A city should have at least 25% green space from an environmental and health point of view. However, quantitative estimation is required to assess the extent and pattern of green space changes for proper urban management. The present study aimed to identify and track the changes in urban green spaces within the Dhaka South City Corporation (DSCC) of Bangladesh over a 30-year period (i.e., 1991–2021). Geospatial techniques were utilized to analyze green space dynamics using Landsat 4–5 TM satellite images from 1991, 2001, and 2011 and Landsat 8 images from 2021. Supervised image classification techniques and Normalized Difference Vegetation Index (NDVI) analysis were performed to assess the urban green space dynamics in DSCC. The results of our study revealed a significant 36.5% reduction in vegetation cover in the DSCC area over the study period. In 1991, the green area coverage in DSCC was 46%, indicating a relatively healthy environment. By 2001, this coverage had declined sharply to 21.3%, further decreasing to 19.7% in 2011, and reaching a low of just 9.5% in 2021. The classified maps generated in the study were validated through field observations and Google Earth images. The outcomes of our study will be helpful for policymakers and city planners in developing and applying appropriate policies and plans to preserve and improve urban green spaces in DSCC in Bangladesh and other Asian megacities with high population density.

In a production environment, delayed stress recognition can impact yield. Imaging can rapidly and effectively quantify stress symptoms using indexes such as normalized difference vegetation index (NDVI). Commercial systems are effective but cannot be easily customized for specific applications, particularly post-processing. We developed a low-cost customizable imaging system and validated the code to analyze images. Our objective was to verify the image analysis code and custom system could successfully quantify the changes in plant canopy reflectance. ‘Supercascade Red’, ‘Wave© Purple’, and ‘Carpet Blue’ Petunias (Petunia × hybridia) were transplanted individually and subjected to increasing fertilizer treatments and increasing substrate pH in a greenhouse. Treatments for the first trial were the addition of a controlled release fertilizer at six different rates (0, 0.5, 1, 2, 4, and 8 g/pot), and for the second trial, fertilizer solution with four pHs (4, 5.5, 7, and 8.5), with eight replications with one plant each. Plants were imaged twice a week using a commercial imaging system for fertilizer and thrice a week with the custom system for pH. The collected images were analyzed using an in-house program that calculated the indices for each pixel of the plant area. All cultivars showed a significant effect of fertilizer on the projected canopy size and dry weight of the above-substrate biomass and the fertilizer rate treatments (p < 0.01). Plant tissue nitrogen concentration as a function of the applied fertilizer rate showed a significant positive response for all three cultivars (p < 0.001). We verified that the image analysis code successfully quantified the changes in plant canopy reflectance as induced by increasing fertilizer application rate. There was no relationship between the pH and NDVI values for the cultivars tested (p > 0.05). Manganese and phosphorus had no significance with chlorophyll fluorescence for ‘Carpet Blue’ and ‘Wave© Purple’ (p > 0.05), though ‘Supercascade Red’ was found to have significance (p < 0.01). pH did not affect plant canopy size. Chlorophyll fluorescence pixel intensity against the projected canopy size had no significance except in ‘Wave© Purple’ (p = 0.005). NDVI as a function of the projected canopy size had no statistical significance. We verified the ability of the imaging system with integrated analysis to quantify nutrient deficiency-induced variability in plant canopies by increasing pH levels.

Land surface temperature (LST) and its interaction with normalized difference vegetation index (NDVI) is crucial for better understanding of environmental changes in current scenario. However, very few or scanty research on the interrelationship between LST, NDVI and topographic elements has been done in India. Therefore, the purpose of conducting this study was to examine, how LST and NDVI change as a function of elevation in Rajasthan. In present study, MODIS derived NDVI and LST and digital elevation model (DEM) from shuttle radar topography mission (SRTM) have been used. Results revealed that the LST and NDVI both were significantly influenced by elevation. Elevation, NDVI and LST varied from -6 to 1698 m, -0.09 to 0.65 and 24 to 45°C throughout the study region. In contrast to LST, which has a decreasing gradient from western to eastern portions, the spatial variability of NDVI has decreasing gradients from southern and eastern to western regions. The highest mean LST value (39.76 ± 0.2.9 0C) was obtained at an elevation range of -6 to 168 m, whereas NDVI value (0.38 ± 0.06) at elevation ranges of 589 – 1698 m. The analysis of the correlations between LST, NDVI and elevation indicated that the elevation has strong positive correlation with NDVI (r2 = 0.26) and negative correlation with LST (r2 = 0.28). Findings from this kind of research can be utilized as a platform for environmental and land use planning for sustainable ecosystem management.

Nigeria’s forest reserves no matter where they are located act as the last succour for the entire citizenry by rendering ubiquitous services that significantly contribute to her economy. This study was intended at assessing the Land Surface Temperature (LST) in Owo Forest Reserve Area (FRA) with a view to sustainable forest management. The essential objectives set for the research include: (i.) assessing the vegetation changes in Owo Forest Reserve Area (ii.) evaluating the LST in Owo forest reserve and (iii.) relating changes in vegetation cover to LST in other to ascertain whether the observed difference in vegetation cover has significant effect and contribution either positively or otherwise to LST values obtained in Owo forest reserve. Recorded spatial coordinates of some selected points constitute the primary data while the secondary data were the satellite dataset, which includes: the Operational Landsat Imager, Enhanced Thematic Mapper, and Thematic Mapper of different years (1991, 2002, 2014, and 2020) used for mapping the vegetation change in the forest reserves. Specifically, thermal bands of the Landsat image in conjunction with the Normalized Difference Vegetation Index (NDVI) were utilized for mapping the LST which is the hallmark of the research. Various data acquired were processed according to the best practices. The results obtained showed that moderate vegetation (shrubs and scattered trees) has been increasing, which signifies that these categories of vegetation in Owo forest reserve area is not depleting for the whole period of study. It was also discovered from the results garnered from 1991 to 2020 that areas with vegetation (Dense, moderate, and sparse) had the least LST values while the forecast probable future LST values for the year 2030 are in the purview of 24.22°C(minimum) and 53.98°C (maximum) which may have negative implications on the forest reserve. The research recommends a significant increase in the rate of tree planting and preserving green areas (vegetation) in order to mitigate the upsurge of LST while laws guiding illegal logging should be upheld with the utmost tenacity.

Abstract Vegetation plays a crucial role in nature, with intricate interactions between it and the geographical environment. The Yangtze River Basin (YRB) refers to the third largest river basin globally and an essential ecological security barrier in China. Monitoring vegetation dynamics in the basin is of profound significance for addressing climate change, soil erosion, and biodiversity loss in the basin's ecosystems. Here, we investigate the spatiotemporal variations of vegetation at both the basin and land cover scales in the YRB from 2000 to 2020. We elucidate the determinants driving the changes and explore future normalized difference vegetation index (NDVI) trends. The results indicate that NDVI in the YRB increased at a rate of 0.0032 year−1 (p

Mangroves are imperative to coastal systems, providing security against extreme weather events by acting as natural barriers. These halophytic plants play a crucial role in mitigating global warming and act as an invaluable resource for consumption. Despite proving to be resilient, mangroves exhibit sensitivity to climatic (e.g., Land Surface Temperature, Salinity, etc.) and man-made factors (e.g., Land Use/Land Cover Changes). Numerous past studies recording the relationship between mangrove growth & development with the aforesaid constituents, but those were mostly restricted to visual observation/pattern recognition and single type of regression analysis. Also, the evaluation of simultaneous exploration of multiple aspects influencing mangrove evolution was not prominent. Therefore, the main objective of this study was to focus on the impact of both salinity and land surface temperature on mangrove biomass by the joint-venture of remote sensing, geographic information system and several machine learning algorithms. The study considered appropriate mangrove site selections with pre-processing of the acquired satellite images. Also, mathematical computations were performed on the raster layers to determine the previously mentioned natural aspects. Finally, several types of regression analysis were conducted to delineate potential patterns, governing mangrove vegetation health by virtue of temperature and salinity. Mangroves’ relationship with temperature and salinity showed insignificant coefficient of determination. However, the generated response curves postulated that high mangrove biomass could be achieved for a specific temperature window (30-33◦C) and vegetation health could deteriorate at increasing salinity. Overall, combined effects of surface temperature and salinity on mangrove vegetation were significantly more (i.e., Maximum coefficient of determination of 0.31) than individual component alone.

Canopy imaging offers a non-destructive, efficient way to objectively measure canopy size, detect stress symptoms, and assess pigment concentrations. While it is faster and easier than traditional destructive methods, manual image analysis, including segmentation and evaluation, can be time-consuming. To make imaging more widely accessible, it’s essential to reduce the cost of imaging systems and automate the analysis process. We developed a low-cost imaging system with automated analysis using an embedded microcomputer equipped with a monochrome camera and a filter for a total hardware cost of ~USD 500. Our imaging system takes images under blue, green, red, and infrared light, as well as chlorophyll fluorescence. The system uses a Python-based program to collect and analyze images automatically. The multi-spectral imaging system separates plants from the background using a chlorophyll fluorescence image, which is also used to quantify canopy size. The system then generates normalized difference vegetation index (NDVI, “greenness”) images and histograms, providing quantitative, spatially resolved information. We verified that these indices correlate with leaf chlorophyll content and can easily add other indices by installing light sources with the desired spectrums. The low cost of the system can make this imaging technology widely available.

The Tibetan Plateau (TP), with its unique geographical and climatic conditions, holds a significant role in global climate change. Therefore, it is particularly urgent to fully understand its vegetation phenology. Herbaceous plants are widely distributed in the TP. However, previous studies have predominantly examined the impact of air temperature on the end date of the vegetation growing season (EOS), with less emphasis on the influence of land surface temperature (LST). In this study, the dynamic changes in the EOS from 2001 to 2020 were analyzed by utilizing the Normalized Difference Vegetation Index (NDVI) data published by NASA. Furthermore, the impact of climate change on the EOS was examined, and the dominant factor (air temperature, LST, or precipitation) influencing the EOS was identified. The main findings were as follows: the average annual EOS predominantly occurred between day of year (DOY) 240 and 280, with an advance from the edge of the plateau to the center. The EOS across the entire region displayed a marginal tendency towards delay, with an average rate of 0.017 days/year. Among all vegetation, shrubs showed the most pronounced delay at a rate of 0.04 days/year. In terms of precipitation, the impact of climate change increased precipitation in both summer and autumn, which could delay EOS. In terms of temperature, an increase in summer Tmin, autumn air temperatures and summer LST delayed the EOS, while an increase in autumn LST advanced the EOS. Compared to air temperature and precipitation, LST had a stronger controlling effect on the EOS (the largest pixel area dominated by LST). These results could offer new insights for enhancing the parameters of vegetation phenology models across the TP.

Climate change has intensified the need for robust fire prevention strategies. Sustainable forest fuel management is crucial in mitigating the occurrence and rapid spread of forest fires. This study assessed the impact of vegetation clearing and/or grazing over a three-year period in the herbaceous and shrub parts of a Mediterranean oak forest. Using high-resolution multispectral data from an unmanned aerial vehicle (UAV), four flight surveys were conducted from 2019 (pre- and post-clearing) to 2021. These data were used to evaluate different scenarios: combined vegetation clearing and grazing, the individual application of each method, and a control scenario that was neither cleared nor purposely grazed. The UAV data allowed for the detailed monitoring of vegetation dynamics, enabling the classification into arboreal, shrubs, herbaceous, and soil categories. Grazing pressure was estimated through GPS collars on the sheep flock. Additionally, a good correlation (r = 0.91) was observed between UAV-derived vegetation volume estimates and field measurements. These practices proved to be efficient in fuel management, with cleared and grazed areas showing a lower vegetation regrowth, followed by areas only subjected to vegetation clearing. On the other hand, areas not subjected to any of these treatments presented rapid vegetation growth.

The variation in the vegetation pattern reflects the change in the regional environment. Normalized Difference Vegetation Index (NDVI) data from 2000 to 2022 for the Upper Bhima sub-basin in Western India has been used to identify the response of vegetation to the El Niño-Southern Oscillations (ENSO) and Indian Ocean Dipole (IOD) events. As a novelty, the present study identifies the ENSO-sensitive and IOD-sensitive vegetation areas within the watershed using vegetation mean to difference anomalies. Monthly NDVI anomalies are used to determine sensitive pixels of vegetation using mean monthly NDVI. Local spatial autocorrelation (LISA) is performed to analyze the pattern of the NDVI anomalies and cluster maps are generated. The results of spatial variation show that NDVI is adversely affected in El Niño years. During La Niña years, the percentage area covered by dense vegetation is more than 80%, which is significantly higher than that of El Niño years in the monsoon and post-monsoon periods. Positive IOD years show significantly more sparse vegetation cover than negative IOD years. The results of LISA analysis show that the rainfall shadow zone in the study area has a cluster of negative sensitive pixels even in the monsoon and post-monsoon period except in negative IOD year. HIGHLIGHTS Vegetation is adversely affected by El Niño and not affected during La Niña.; The novelty is to identify the ENSO-sensitive and IOD-sensitive vegetation areas.; A positive IOD has an adverse effect on vegetation compared to a negative IOD event.; Rainfall shadow zone has negative sensitive pixels cluster even in monsoon and post-monsoon period.;

In the context of the national strategy of “carbon peak and carbon neutrality”, ecological restoration in resource-depleted cities is becoming an important way to carbon sequestration and sinks. The quantitative evaluation of carbon sequestration and sink capacity using long time series observation data will provide valuable references for designing ecological restoration methods in resource-depleted cities. Taking Xuzhou, a typical resource-depleted transformation city in eastern China, as the research area, the temporal and spatial variation characteristics of Gross Primary Productivity (GPP) were observed and analyzed by “Eddy Covariance” and “satellite remote sensing”. The results show that: ① MODIS satellite remote sensing underestimates GPP during the growing season when vegetation is actively growing, because the Light Use Efficiency model sets the maximum Light Use Efficiency of the same vegetation as a constant value, which does not reflect the influence of vegetation growth state, thus increasing the uncertainty of estimating Light Use Efficiency. However, the MODIS GPP and the GPP measured by Eddy Covariance flux tower have the same trend and strong correlation (R2=0.587, P=0), so the MODIS GPP data can be used for the analysis of the GPP change characteristics before and after ecological restoration in the Xuzhou mining area. ② From 2005 to 2011, the coal mining severely damaged the ecological environment in the Xuzhou mining area, and the carbon sequestration capacity continued to weaken. After 2011, the Xuzhou area adopted ecological restoration measures, and the GPP increased significantly and reached the highest value in 2015 (0.878 kgC/(m2·a)), and the change of GPP tended to be stabilized between 2016 and 2020 (0.85 kgC/(m2·a) or so). ③ A series of ecological restoration measures such as mine closure, coal gangue backfill and ecological park construction adopted by Xuzhou have improved the carbon sequestration and sink capacity of the original mine area and formed a self-stabilizing ecosystem, and there is still a trend of improvement in the future, proving that the ecological restoration of coal depleted cities can effectively improve the local ecological environment and rapidly enhance the local carbon sequestration capacity of vegetation within 5 to 10 years, providing a reference for the ecological restoration of other coal depleted cities.

Spatial normalized difference vegetation index finds various applications in crop monitoring and prediction. Although this index is mainly aimed to represent the state of vegetation cover, it is suggested that it could be utilized for other remote monitoring purposes, for example, soil humus content monitoring. The study was carried out in 2022-2023 fallow-field period in Kherson oblast, the South of Ukraine, to establish the relationship between the values of bare-soil normalized difference vegetation index and content of humus in the soils of the region. Statistical modeling was performed using the best subsets regression analysis in BioStat v.7 and artificial neural network with back propagation of error algorithm in Tiberius XL. The best performance was recorded for the combined model of cubic regression and artificial neural network, with moderate fitting quality (coefficient of determination is 0.29), and good prediction accuracy (mean average percentage error is 13.22%). The results approve the suggestion of possibility of spatial vegetation index use in soil state monitoring, especially, if further scientific work enhances the fitting quality of the model.

Traffic injury caused by foot- or athlete-surface interaction is one of the most critical problems athletic field managers face in maintaining the surface playability and aesthetic quality of athletic fields. Bermudagrass (Cynodon spp.) is the most widely used turfgrass species on athletic fields in the transitional climatic zone. A 2-year field study was conducted to evaluate nine bermudagrass cultivars for their persistence and surface playability under simulated fall cleat traffic. The experiment was conducted in Stillwater, OK, on a natural loam soil. Treatments were arranged as a split-block design with three replications. Traffic was applied for 6 weeks in Fall 2019 and 2020 using a Baldree traffic simulator, which generated 10 traffic events per week; each traffic event resulted in 678 cleat marks/m2. ‘Bimini’ was generally found to be the most persistent grass under traffic for aesthetic properties, and ‘Astro’ and ‘Tifway’ were the least persistent. Surface playability was affected by simulated traffic stress as shear strength (SS) declined and surface hardness (SH) increased, over time. ‘Bimini’ had greater SS than ‘Astro’ and ‘OKC1131’ (Tahoma 31®) by 1.9 and 1.4 N·m, respectively. SS of ‘DT-1’ (TifTuf®) and Tahoma 31 and SH of ‘OKC1134’ (NorthBridge®) were least affected by simulated traffic stress. Overall, surface playability characteristics of NorthBridge, ‘Bimini’, ‘OKC1119’ (Latitude 36®), TifTuf, Tahoma 31, and ‘Riley Riley’s Super Sport’ (Celebration®) were least affected by traffic. Findings illustrate bermudagrass cultivars can vary in visual persistence and surface playability.

Assessing groundwater potential for sustainable resource management is critically important. In addressing this concern, this study aims to advance the field by developing an innovative approach for Groundwater potential zone (GWPZ) mapping using advanced techniques, such as FuzzyAHP, FuzzyDEMATEL, and Logistic regression (LR) models. GWPZ was carried out by integrating various primary factors, such as hydrologic, soil permeability, morphometric, terrain distribution, and anthropogenic influences, incorporating twenty-seven individual criteria using multi-criteria decision models along with a hybrid approach for the Subarnarekha River basin, India, in Google earth engine (GEE). The predictive capability of the model was evaluated using a Multi-Collinearity test (VIF

The destructive and empirical methods commonly used to estimate carbon pools in forests managed timber are time-consuming, expensive and unfeasible at a large scale; satellite images allow evaluations at different scales, reducing time and costs. The objective of this study was to evaluate the tree biomass (TB) and carbon content (CC) through satellite images derived from Sentinel 2 in underutilized stands in southern Mexico. In 2022, 12 circular sites of 400 m2 with four silvicultural treatments (STs) were established in a targeted manner: 1st thinning (T1), free thinning (FT), regeneration cut (RC) and unmanaged area (UA). A tree inventory was carried out, and samples were obtained to determine their TB based on specific gravity and CC through the Walkey and Black method. The satellite image of the study area was downloaded from Sentinel 2 to fit a simple linear model as a function of the Normalized Difference Vegetation Index (10 m pixel−1) showing significance (p ≤ 0.01) and a adjusted R2 = 0.92. Subsequently, the TB and CC (t ha−1) were estimated for each ST and managed area. The total managed area (3,201 ha−1) had 126 t TB ha−1 and 57 t C ha−1. Of the areas with STs, the area with FT showed the highest accumulation of TB (140 t ha−1) and C (63 t ha−1) without showing differences (p > 0.05) with respect to those of the UA, which presented 129 t TB ha−1 and 58 t C ha−1. The satellite images from Sentinel 2 provide reliable estimates of the amounts of TB and CC in the managed stands. Therefore, it can be concluded that an adequate application of STs maintains a balance in the accumulation of tree C.

IntroductionThe number of chronic respiratory disease (CRD) individuals worldwide has been continuously increasing. Numerous studies have shown that greenness can improve chronic respiratory health issues through different mechanisms, with inconsistent evidence. By quantitatively summarizing existing studies, our purpose is to determine the connection between greenness exposure and various chronic respiratory health.MethodsWe conducted a comprehensive search on PubMed, EMBASE, and Web of Science core databases to identify relevant studies on the correlation between greenness exposure and chronic respiratory health issues. Studies published up to January 2023 were included in the search. The study used the most frequent indicator (normalized difference vegetation index [NDVI]) as the definition of greenness exposure.ResultsWe finally identified 35 studies for meta-analysis. We calculated pooled effects across studies using a random-effects model and conducted a subgroup analysis by age and buffer zones to discuss the effects on chronic respiratory health issues. This study showed that 0.1 increments in NDVI were significantly related to lower rates of asthma incidence, lung cancer incidence, and chronic obstructive pulmonary disease (COPD) mortality risk; the pooled RRs were 0.92 (95% CI: 0.85–0.98), 0.62 (95% CI: 0.40–0.95), and 0.95 (95% CI: 0.92– 0.99), respectively. For the age subgroup, the higher greenness exposure level was related to the incidence rate of asthma among teenagers aged 13–18years (RR: 0.91; 95% CI: 0.83–0.99). For the buffer subgroup, a positive relationship with greenness exposure and asthma incidence/prevalence at 200–300m and 800– 1000m buffers, as well as the COPD mortality at 800–1000m buffer, the pooled RRs were 0.92 (95% CI: 0.86–0.98), 0.87 (95% CI: 0.81–0.93), and 0.93 (95% CI: 0.88– 0.98), respectively. Evidence of publication bias was not detected in this study.DiscussionOur study is the first global meta-analysis between greenness and various CRDs to report an inverse association. Further research is needed in order to determine the effect of greenness exposure on different CRDs. Therefore, when planning for green development, more consideration must be given to public health and green management as intervention measures.https://www.crd.york.ac.uk/PROSPEROFILES/384029_STRATEGY_20230116.pdf

ABSTRACTSoil moisture estimation is a key component in hydrological processes and irrigation amounts' estimation. The synergetic use of optical and radar data has been proven to retrieve the surface soil moisture at a field scale using the Water Cloud Model (WCM). In this work, we evaluate the impact of staellite-derived vegetation descriptors to estimate the surface soil moisture. Therefore, we used the Sentinel-1 data to test the polarization ratio ([Formula: see text]) and the normalized polarization ratio (IN) and the frequently used optical Normalized Difference vegetation Index (NDVI) as vegetation descriptors. Synchronous with Sentinel-1 acquisitions, in situ soil moisture were collected over wheat fields in the Kairouan plain in the center of Tunisia. To avoid the bare soil roughness effect and the radar signal saturation in dense vegetation context, we considered the data where the NDVI values vary between 0.25 and 0.7. The soil moisture inversion using the WCM and NDVI as a vegetation descriptor was characterized by an RMSE value of 5.6 vol.%. A relatively close performance was obtained using IN and ([Formula: see text]) with RMSE under 7. 5 vol.%. The results revealed the consistency of the radar-derived data in describing the vegetation for the retrieval of soil moisture.

Forests and wetlands played a crucial role in minimizing the increasing trends of land surface temperature (LST). This study aimed to analyze the contribution of forests and wetlands on LST in Yayo district using multi-spectral and multi-temporal satellite. Landsat images of 1986, 2003 and 2021 were downloaded from United State Geological Survey website with 30 m spatial resolution. Results show that the mean LST was increased from 22.8 °C in 1986 to 27 °C in 2003 over cultivated land. As the results of forest conservation and wetland protection, the LST in Yayo district is experencing a declining trend in the recent year. Result reveal that the normalized difference vegetation index and Normalized Difference Water Index show a strong negative relationship with LST. Over the last two decades, the LST was declined by 1.5 °C due to the protection of forest and wetland ecosystem. Thus, governmental and non-governmental organization should encourage the conservation and protection of forest and wetland ecosystems.

Fall armyworm (FAW) Spodoptera frugiperda (J.E. Smith), damage was monitored at a regional scale using time series data in Western and Southern African countries. The study employed the normalized difference vegetation index (NDVI) computed from Landsat 8 imagery using the Google Earth Engine (GEE) using image composites for the years 2013 to 2020 for the study areas. The index was then reclassified based on the NDVI threshold values into low, sparse, moderate, and dense classes. FAW prevalence data were then utilized to validate the correlation between the FAW infestation and NDVI values. FAW was associated with a decrease in vegetation productivity between the years 2016, 2017, and 2018 when the pest infestation was reported in the study areas. The validation results showed that there is a correlation between FAW infestation and NDVI (R20.83). Our study highlighted that NDVI can be used as a proxy to quantify pest damage to vegetation productivity.

Land cover changes occur along with development and an increase in urbanization, affecting the region's ecology. Tarogong Kidul Sub-district, Garut District, experiences land cover changes that change land surface temperature. This study aims to determine the relationship between land cover changes and land surface temperature. This study uses Landsat 8 Surface Reflectance Tier 1 satellite imagery to extract Normalized Difference Vegetation Index information and process soil surface temperatures for three periods, 2014, 2017, and 2020 as well as supervised classification methods. Image collection and processing are done via Google Earth Engine and ArcGIS Pro software. The results were in the form of the distribution of land surface temperature correlated with changes in the value of the land cover vegetation index using simple linear regression and spatial correlation analysis. This study reveals that land cover change is closely related to the increase in land surface temperature indicated by the rise in land surface temperature in areas experiencing changes in land use. The results of linear regression analysis (84.49%) confirm that land cover changes and the greenness index of vegetation are the most critical driving factors for changes in land surface temperature.

Land surface temperature (LST) and its relationship with vegetation indices (VIs) have proven to be effective for monitoring water stress in large-scale crops. Therefore, the objective of this study is to find an appropriate VI to analyse the spatio-temporal evolution of olive water stress using LST images and VIs derived from Landsat 5 and 8 satellites in the semi-arid region of southern Peru. For this purpose, VIs (Normalised Difference Vegetation Index (NDVI), Enhanced Vegetation Index 2 (EVI2) and Soil Adjusted Vegetation Index (SAVI)) and LST were calculated. The information was processed in Google Earth Engine (GEE) for the period 1985 to 2024, with an interval of every five years for the summer season. The triangle method was applied based on the LST-VIs scatterplot analysis, a tool that establishes wet and dry boundary conditions for the Temperature Vegetation Dryness Index (TVDI). The results indicated a better appreciation of olive orchard water stress over time, with an average of 39% drought (TVDINDVI and TVDISAVI), 24% severe drought (TVDINDVI) and 25% (TVDISAVI) of the total area, compared to TVDIEVI2, which showed 37% drought and 16% severe drought. It is concluded that TVDINDVI and TVDISAVI provide a better visualisation of the water stress map of the olive crop and offer a range of options to address current and future problems in water resource management in the olive sector in semi-arid areas of southern Peru.

Recently, the frequent occurrence of droughts has caused a serious impact on vegetation growth and progression. This research is based upon the normalized difference vegetation index (NDVI) from 2001 to 2020. The correlation between the NDVI and standardized precipitation evapotranspiration index (SPEI) at disparate time scales was used to assess the response of vegetation growth to drought in the Yinshanbeilu region. The drought levels of SPEI1, SPEI3, SPEI6, and SPEI12 increased prominently in the eastern region of the country, while the NDVI decreased significantly from east to west in spring, summer, and autumn but was reversed in the winter. The area with an upward trend (33.86%) was slightly lower than that with a downward trend (66.14%). The correlation coefficients between the NDVI and SPEI over the entire year increased with the SPEI timescale. The elevated values were concentrated in the southeastern and western regions of the survey region. Additionally, the best correlation timescales were SPEI6 and SPEI12. Grassland was the most sensitive vegetation type to the SPEI response in the NDVI. The correlation coefficients of NDVI and SPEI1–12 were 0.313, 0.459, 0.422, and 0.406. Both spring and summer were more responsive to SPEI12, whereas autumn and winter were more responsive to SPEI3. The correlation of disparate time scales exhibited complex soil texture features with respect to different seasonal scales, and the soil texture showed a strong response to vegetation in both summer and autumn. Loam, sandy loam, and silty loam all exhibited the highest response to SPEI12, with coefficients of 0.509, 0.474, and 0.403, respectively.

The Normalized Difference Vegetation Index (NDVI) is widely used for monitoring vegetation status, as accurate and reliable NDVI time series are crucial for understanding the relationship between environmental conditions, vegetation health, and productivity. Ground digital cameras have been recognized as important potential data sources for validating remote-sensing NDVI products. However, differences in the spectral characteristics and imaging methods between sensors onboard satellites and ground digital cameras hinder direct consistency analyses, thereby limiting the quantitative application of camera-based observations. To address this limitation and meet the needs of vegetation monitoring research and remote-sensing NDVI validation, this study implements a novel NDVI camera. The proposed camera incorporates narrowband dual-pass filters designed to precisely separate red and near-infrared (NIR) spectral bands, which are aligned with the configuration of sensors onboard satellites. Through software-controlled imaging parameters, the camera captures the real radiance of vegetation reflection, ensuring the acquisition of accurate NDVI values while preserving the evolving trends of the vegetation status. The performance of this NDVI camera was evaluated using a hyperspectral spectrometer in the Hulunbuir Grassland over a period of 93 days. The results demonstrate distinct seasonal characteristics in the camera-derived NDVI time series using the Green Chromatic Coordinate (GCC) index. Moreover, in comparison to the GCC index, the camera’s NDVI values exhibit greater consistency with those obtained from the hyperspectral spectrometer, with a mean deviation of 0.04, and a relative root mean square error of 9.68%. This indicates that the narrowband NDVI, compared to traditional color indices like the GCC index, has a stronger ability to accurately capture vegetation changes. Cross-validation using the NDVI results from the camera and the PlanetScope satellite further confirms the potential of the camera-derived NDVI data for consistency analyses with remote sensing-based NDVI products, thus highlighting the potential of camera observations for quantitative applications The research findings emphasize that the novel NDVI camera, based on a narrowband spectral design, not only enables the acquisition of real vegetation index (VI) values but also facilitates the direct validation of vegetation remote-sensing NDVI products.

Natural springs, recognized as biodiversity hotspots and keystone ecosystems, exert positive ecological influences beyond their immediate extent, particularly in dryland environments. The water feeding these springs, largely governed by natural climatic conditions, is susceptible to anthropogenic impacts. The objective of this study was to determine the factors that cause fluctuations in water availability to springs of the hyper-arid Arava Valley (Israel/Jordan). Using the Standard Precipitation Index, we statistically classified the historical record of yearly rainfall for the past four decades into clusters of dry and wet sub-periods. We assessed changes in vegetation cover around the springs using the Landsat-derived Normalized Difference Vegetation Index (NDVI) for each sub-period. To assess the anthropogenic effects, we examined the correlations between vegetation cover, water extraction from the aquifer, and the status of adjacent agricultural plots that share a hydrological connection with the springs. Our findings revealed fluctuations between wet and dry sub-periods over the last four decades. We observed high responsiveness of vegetation cover around the springs to these fluctuating sub-periods. Of the 25 studied springs, 12 were directly influenced by anthropogenic factors—7 experienced a decline in vegetation, which we attributed to water extraction from the aquifers, while vegetation increase in 5 springs was attributed to water seepage from agricultural areas upstream. In conclusion, addressing vital habitats such as natural springs in arid drylands requires a holistic approach that integrates long-term climatic, ecological, and anthropogenic observations.

An arid climate is a unique condition that has a significant impact on the growth of crops and natural vegetation. The normalized difference vegetation index (NDVI) is a crucial remotely sensed measurement of greenness due to its strong correlation with crop and vegetation growth and productivity. In the present study, the spatiotemporal dynamics of NDVI were analyzed from 2000 to 2021 in the segment of the arid western plain zone of Rajasthan, India. NDVI time-series data, as well as data related to climatic factors, viz., precipitation, soil moisture, evapotranspiration, and 2-m air temperature, were collected from Giovanni, the Goddard Earth Science dataset. The Mann–Kendall (MK) trend test and Sen’s slope depicted the long-term continuous time–frequency trend, while Karl Pearson’s correlation analysis depicted the significant relationship between all the factors except 2-m air temperature. The seasonal and mean monthly results of all the factors except 2-m air temperature showed considerable coherence with NDVI. The multiscale time–frequency decomposition or wavelet analysis depicted the fifth to the seventh month and the ninth to the 15th month of the cycle, showing the significance of the cropping pattern and the natural vegetation growth cycle. The cross-wavelet analysis further depicted important coherence, leading, and lagging phases among climatic factors and NDVI. Our research provided significant insights into the long-term variability and coherence of various climatic factors with NDVI that are applicable on regional and global scales.

Aerosols have a severe impact on the Earth's climate, human health, and ecosystem. To understand the impacts of aerosols on climate, human health, and the ecosystem we must need to understand the variability of aerosols and their optical properties. Therefore, we used Aqua-MODIS retrieved aerosol optical depth (AOD) (550 nm) and Angstrom exponent (AE) (440/870) data to analyze the Spatio-temporal seasonal variability of aerosols and their relationship with different meteorological parameters over Pakistan from 2002 to 2021. High (>0.5) AOD values were observed during the summer season and low (1) in the northern regions of Pakistan indicating the dominance of fine mode particles during the winter season. Moreover, AOD showed a positive correlation with Relative Humidity (RH), Evapotranspiration, Wind speed (WS), and Temperature. On the other hand, it showed a negative correlation with Soil moisture (SM), Normalized difference vegetation index (NDVI), and precipitation over Pakistan. Therefore, considering the outcomes of this study will help policymakers to understand the spatiotemporal variability of aerosols and their seasonal correlation with different meteorological parameters.

The purpose of this research is to reveal the internal relationship among community green space, space perception, and activity behavior response to supplement the lack of research results on the binary relationship between green space and behavior. Nine residential community green spaces and 398 residents were selected as the research objects. Thematic clustering and factor identification were used to determine the spatial dimensions of community green space that residents were concerned about. The analysis of the green exposure index, spatial perception evaluation, and activity behavior survey were combined to determine the influence of the green exposure index on spatial perception and activity behavior and its internal correlation path. According to research data, the community green view index (GVI) and normalized difference vegetation index (NDVI) negatively affected the perception factor, while the perception factor positively affected the activity frequency. The SEM model shows that the green exposure index stimulated activity behavior through the intermediate effect of the internal perception path of perceived landscape quality, perceived workability, and perceived accessibility. Spatial perception as the basis of the instantaneous emotional reaction process may affect people's choices for activities but be unable to extend the duration of the activities. The internal association among community green space, spatial perception, and physical activity behavior develops on the basis of spatial patterns at certain scales. This study provides a theoretical basis for understanding the spatial experience and residents' behavioral needs, evaluating the quality of urban green space scientifically, and promoting the optimization of community green space structure.

Under the background of global climate change, characterizing correlations between vegetation dynamics and bioclimatic indices (calculations based on temperature, precipitation, and reference evapotranspiration) are important for vegetation conservation and the restoration of fragile ecosystems. The Southwest China (SWC), which is a hotspot of global biodiversity with complex and diverse terrain, stereoscopic climatic characteristics, and pronounced spatial heterogeneity. Here, spatial–temporal changes in bioclimatic factors and annual normalized difference vegetation index (NDVI) and the effects of bioclimatic factors on the NDVI of different vegetation types at different elevations in SWC were examined during 1961(1982)–2019. Temperature-related bioclimatic indices had significant positive and negative effects on the NDVI of different types of vegetation, and the effects of temperature-related bioclimatic indices were stronger than those of precipitation and reference evapotranspiration-related indices in the three subregions. The effects of changes in temperature and reference evapotranspiration-related bioclimatic indices on the NDVI of different types of vegetation were significant in different elevation bins in the three subregions. The rate of change in high-value and low-value NDVI pixels of different types of vegetation significantly increased and decreased over the study period in the three subregions, respectively. The NDVI of grass vegetation significantly increased in the three subregions with elevation, and it was highest in the higher elevation bins. The interaction effects of several bioclimatic factors on the NDVI of different vegetation types varied, and the responses of the same vegetation type to interactions between different bioclimatic factors also varied. These findings provide new insights into the complex mechanisms underlying the relationships between bioclimatic indices and vegetation dynamics and have implications for the conservation of vegetation and the restoration of fragile ecosystems.

Under long-term agricultural water saving strategy implemented in the Yellow River basin (YRB), this study analyzed long-term regional groundwater responses and their ecological impacts in an arid irrigation district (Hetao) in the upper YRB. The Mann-Kendall trend test was adopted to examine the trends of the monitoring data of climatic-anthropogenic factors and groundwater depth-salinity, as well as the modeling and remote-sensing results of natural vegetation growth. Results indicated significant decreasing net water diversion while increasing drainage since agricultural water saving, which is a major cause of groundwater reduction instead of climatic factors. This reduction was further confirmed with significant increasing groundwater depth (GWD) trends found in about 90 out of the total 224 monitoring wells across Hetao during 1970–2015. An overall salt accumulation was recognized for Hetao, but groundwater salinity only significantly changed in a few areas with insignificant trends found for more than 70 out of the total 114 monitoring wells during 1995–2015. Meanwhile, significant large-scale decreasing trends were detected for both simulated natural vegetation biomass and remote-sensing derived vegetation index, and wetland loss problem was also found. Particularly, relatively strong similarities were found among the spatial distributions of significant increasing GWD, decreasing vegetation biomass and index, and losing wetlands. This further suggested that the reduction might have caused negative ecological impacts. Overall, the results provided important information regarding long-term regional groundwater responses and their ecological impacts under agricultural water saving, which is significant for agricultural water management for arid irrigation districts.

Drought has serious impacts on agricultural production and ecological environment in China. Study on the causes of drought and drought monitoring technology is crucial for drought mitigation. This study conducts drought monitoring based on temperature vegetation dryness index (TVDI) and investigates with the impact of anthropogenic pressure in the Poyang Lake basin (PYLB). In the study, the MODIS normalized vegetation index (NDVI) and land surface temperature (LST) products from 2000 to 2021 were selected to construct NDVI-LST feature space processed with elevation correction, from which the monthly TVDI data was calculated. The land use data and nighttime light data were used to construct the anthropogenic pressure index (API). The results indicate that the frequency of drought in the PYLB is higher in the south and lower in the north. According to drought influence scope (DIS), the occurrence frequency of basin-wide drought (DIS ≥ 50%) in the PYLB is the highest, followed by regional drought (33%≤DIS

The preparation of land cover maps provides the possibility of studying the impact of land surface changes on sustainable development and is significant for a wide range of important issues at the global level. The current research aims to facilitate the preparation of land cover maps using the classification of Normalized Difference Vegetation Index (NDVI) values ​​and prepare land cover maps from it. For this purpose, first, two complete consecutive Landsat-8 scenes of parts of Iran and Turkmenistan were selected for August 30, 2021. Then the images were classified using supervised classification algorithms including Neural Network Classification (NNC), maximum Likelihood Classification (MLC), Support Vector Machine (SVM), Minimum Distance (MinD) and Mahalanobis Distance (MahD). In the next step, to perform an evaluation, by using a thousand ROI for a test, the overall accuracy, kappa coefficient, user accuracy and producer accuracy of the map produced by each of the algorithms were calculated. Then, using the most optimal algorithm, the threshold of NDVI image values ​​was extracted in order to classify it and the obtained map was re-evaluated for accuracy. Among the evaluated algorithms, the MLC algorithm had the most optimal performance with a kappa coefficient of 0.75 and overall accuracy of 80.86%. The results of evaluating the accuracy of the NDVI Based land cover Classification (NBC) index also indicated that this map has extracted the land cover map with an overall accuracy of 83% and a Kappa coefficient of 0.77. This index showed good performance in the classification of Bare Land Class (BLC), Water Area Class (WAC) and Salt Marsh Class (SMC) with user accuracy and producer accuracy above 94%. This is while the Agricultural Land Class (ALC) and Vegetation Class (VC) were classified by this index with producer accuracy of above 73% and user accuracy of 69% and 97%, respectively. The results of this research indicate the acceptable accuracy of NDVI index values ​​for the production of natural land cover maps and can be used in order to prepare these maps for geographic monitoring and achieving sustainable environmental development.

This study explores drones’ applications and proposes a cost-effective drone monitoring system for both palm trees and street lighting networks. The planned drone technical system has two monitoring sections. First, a model is developed to examine the health of date palm trees, in which drone photos are used to determine whether palm trees are suffering from diseases such as black scorch and sudden decline syndrome. These images are transferred into a central computer to stimulate normalized difference vegetation index (NDVI) models using AgiSoft software. The simulated NDVI models indicated that there are no health issues with date palm trees, which has resulted in the positive feedback in terms of the economic growth. Second, drone technology is utilized to detect the technical faults in the lighting network to ensure proper maintenance and social security. Twelve images of street lights are captured to demonstrate the working condition and the operational status of the street lights. These images are processed in MATLAB software, and a stimulated image processing model is implemented to enhance the monitoring of the street lighting network. The simulation findings indicate that the light in one of the images is not functioning, and ArcGIS Pro is utilized to locate it.