Satellite-Derived NDVI Predicts Forage Availability in a Wild Ungulate System: Ground-Truthing Using Field-Collected Vegetation Biomass.

<p>The Spanish Central Range hosts some of the southernmost populations of <em>Fagus sylvatica</em> L. (European beech). Recent cartography indicates that these populations are expanding, going up-streams and gaining ground to oak forests of <em>Quercus pyrenaica </em>Willd., heather-lands, and pine plantations. Understanding the spectral phenology of European beech populations—which leaf flush occurs earlier than other vegetation formations—in this Mediterranean mountain range will provide insights of the species recent dynamics, and will enable modelling its performance under future climate oscillations. Intra-annual series of 211 Landsat OLI/ETM+ images, acquired between April 2013-December 2019, and 217 Sentinel-2A/B images, acquired between April 2017-December 2019, were employed to characterize the spectral phenology of European beech populations and five other vegetation types for comparison in an area of 108000 ha. Vegetation indices (VI) including the Normalized Difference Vegetation Index (NDVI) and Tasseled Cap Angle (TCA) from Landsat, and the NDVI and Enhanced Vegetation Index (EVI) from Sentinel-2 were retrieved from sample pixels. The temporal series of these VI were modelled with Savitzky-Golay and double logistic functions, and assessed with TIMESAT software, enabling the parametric characterization of European beech spectral phenology in the area with the start, length, and end of season, as well as peak time and value. The length of beech phenological season was similar when portrayed by Landsat and Sentinel-2 NDVI time series (214 and 211 days on average for the common period 2017-2019) although start and end differed. Compared with NDVI counterparts the TCA season started and peaked later, and the EVI season was shorter. Sentinel-2 NDVI peaked higher than Landsat NDVI. The European beech had an earlier (21 days on average) start of season than competing oak forests. Joint analysis of data from the virtual constellation Landsat/ Sentinel-2 and calibration with field observations may enable more detailed knowledge of phenological traits at the landscape scale.<em></em></p>

Most ecological studies use remote sensing to analyze broad-scale biodiversity patterns, focusing mainly on taxonomic diversity in natural landscapes. One of the most important effects of high levels of urbanization is species loss (i.e., biotic homogenization). Therefore, cost-effective and more efficient methods to monitor biological communities' distribution are essential. This study explores whether the Enhanced Vegetation Index (EVI) and the Normalized Difference Vegetation Index (NDVI) can predict multifaceted avian diversity, urban tolerance, and specialization in urban landscapes. We sampled bird communities among 15 European cities and extracted Landsat 30-meter resolution EVI and NDVI values of the pixels within a 50-m buffer of bird sample points using Google Earth Engine (32-day Landsat 8 Collection Tier 1). Mixed models were used to find the best associations of EVI and NDVI, predicting multiple avian diversity facets: Taxonomic diversity, functional diversity, phylogenetic diversity, specialization levels, and urban tolerance. A total of 113 bird species across 15 cities from 10 different European countries were detected. EVI mean was the best predictor for foraging substrate specialization. NDVI mean was the best predictor for most avian diversity facets: taxonomic diversity, functional richness and evenness, phylogenetic diversity, phylogenetic species variability, community evolutionary distinctiveness, urban tolerance, diet foraging behavior, and habitat richness specialists. Finally, EVI and NDVI standard deviation were not the best predictors for any avian diversity facets studied. Our findings expand previous knowledge about EVI and NDVI as surrogates of avian diversity at a continental scale. Considering the European Commission's proposal for a Nature Restoration Law calling for expanding green urban space areas by 2050, we propose NDVI as a proxy of multiple facets of avian diversity to efficiently monitor bird community responses to land use changes in the cities.

Phenology of plants is important for ecological interactions. The timing and development of green leaves, plant maturity, and senescence affects biophysical interactions of plants with the environment. In this study we explored the agreement between land-based camera and satellite-based phenology metrics to quantify plant phenology and phenophases dates in five plant community types characteristic of the semi-arid cold desert region of the Great Basin. Three years of data were analyzed. We calculated the Normalized Difference Vegetation Index (NDVI) for both land-based cameras (i.e., phenocams) and Landsat imagery. NDVI from camera images was calculated by taking a standard RGB (red, green, and blue) image and then a near infrared (NIR) plus RGB image. Phenocam NDVI was calculated by extracting the red digital number (DN) and the NIR DN from images taken a few seconds apart. Landsat has a spatial resolution of 30 m2, while phenocam spatial resolution can be analyzed at the single pixel level at the scale of cm2 or area averaged regions can be analyzed with scales up to 1 km2. For this study, phenocam regions of interest were used that approximated the scale of at least one Landsat pixel. In the tall-statured pinyon and juniper woodland sites, there was a lack of agreement in NDVI between phenocam and Landsat NDVI, even after using National Agricultural Imagery Program (NAIP) imagery to account for fractional coverage of pinyon and juniper versus interspace in the phenocam data. Landsat NDVI appeared to be dominated by the signal from the interspace and was insensitive to subtle changes in the pinyon and juniper tree canopy. However, for short-statured sagebrush shrub and meadow communities, there was good agreement between the phenocam and Landsat NDVI as reflected in high Pearson’s correlation coefficients (r > 0.75). Due to greater temporal resolution of the phenocams with images taken daily, versus the 16-day return interval of Landsat, phenocam data provided more utility in determining important phenophase dates: start of season, peak of season, and end of season. More specific species-level information can be obtained with the high temporal resolution of phenocams, but only for a limited number of sites, while Landsat can provide the multi-decadal history and spatial coverage that is unmatched by other platforms. The agreement between Landsat and phenocam NDVI for short-statured plant communities of the Great Basin, shows promise for monitoring landscape and regional-level plant phenology across large areas and time periods, with phenocams providing a more comprehensive understanding of plant phenology at finer spatial scales, and Landsat extending the historical record of observations.

The Moderate Resolution Imaging Spectroradiometer (MODIS) VI products provide consistent, spatial, and temporal comparisons of global vegetation conditions that can be used to monitor the Earth's terrestrial photosynthetic activity. In order to compare and evaluate the ability that the MODIS two indices (VI) , the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) , monitor vegetation over a diverse range of biomes in Northwest China, the MODIS vegetation index products data set were used, which are from NASA LP DAAC(Land Process Distributed Active Archive Center) and have 250m resolution and 16-day composting periods in 2004.Different vegetation types including broadleaf forests, needleleaf forests, meadows, grassland, steppes, scrubs, desert and cultivated vegetation were chosen as representative types, based on Vegetation Map of Northwest China. Two CE-313 radiancements, which is portable instrument and has five filters between 450 and 1650 nm, were used to collect the spectrum data for calculating vegetation indices over different vegetation types in the plants growth season. The levels of vegetation cover were observed at the same spots and the same time when the CE-313 radiancements worked. The results show that the NDVI was higher than the EVI in most part of Northwest China. The difference between the two indices increased from deserts, steppes, cultivated vegetation, meadows to forest. Both NDVI and EVI were well indicated the distribution and the growth of various vegetations in arid and semi-arid area. The NDVI saturated in these high biomass types vegetations, such as broadleaf forests, needleleaf forests, meadows and part of cultivated vegetation, during the plants blooming period. The NDVI did not increase with the growth of these vegetations during the period. The EVI was different. It increased with the growth of vegetation. The NDVI saturation threshold was about 0.8. The length of the period that NDVI saturating was different from 1 to 5 months with the type of vegetation. The length of the period of broadleaf forests was the longest and that of the cultivated vegetation was the shortest. Though the height of high-cold meadows was not higher than forty cm in northeast of Tibetan Plateau, the NDVI of these regions saturated for 2 months during the plants flourished. The data gotten from the CE-313 showed that the NDVI of many kinds vegetation, for example, winter wheat, corn, clover blossom and forb high cold meadows, may saturate during the process of becoming mature. Crossplots of the two Vis showed a curvilinear relationship between them, such that the NDVI always had higher values but appeared to reach an asymptotic maximum value. When the NDVI was about 0.8, the NDVI was almost stop responding with the vegetation density while the EVI was still responding. Both NDVI and EVI have a good linear correlation with the levels of vegetation cover according to the data gotten from the CE-313. The Correlation of EVI was better than that of NDVI especially in the higher levels vegetation cover. The Correlation coefficient of EVI was 0.8112 and the NVDI was 0.6946.

Updated extent, area, and spatial distribution of tropical evergreen forests from inventory data provides valuable knowledge for research of the carbon cycle, biodiversity, and ecosystem services in tropical regions. However, acquiring these data in mountainous regions requires labor-intensive, often cost-prohibitive field protocols. Here, we report about validated methods to rapidly identify the spatial distribution of tropical forests, and obtain accurate extent estimates using phenology-based procedures that integrate the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat imagery. Firstly, an analysis of temporal profiles of annual time-series MODIS Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Land Surface Water Index (LSWI) was developed to identify the key phenology phase for extraction of tropical evergreen forests in five typical lands cover types. Secondly, identification signatures of tropical evergreen forests were selected and their related thresholds were calculated based on Landsat NDVI, EVI, and LSWI extracted from ground true samples of different land cover types during the key phenology phase. Finally, a map of tropical evergreen forests was created by a pixel-based thresholding. The developed methods were tested in Xishuangbanna, China, and the results show: (1) Integration of Landsat and MODIS images performs well in extracting evergreen forests in tropical complex mountainous regions. The overall accuracy of the resulting map of the case study was 92%; (2) Annual time series of high-temporal-resolution remote sensing images (MODIS) can effectively be used for identification of the key phenology phase (between Julian Date 20 and 120) to extract tropical evergreen forested areas through analysis of NDVI, EVI, and LSWI of different land cover types; (3) NDVI and LSWI are two effective metrics (NDVI ≥ 0.670 and 0.447 ≥ LSWI ≥ 0.222) to depict evergreen forests from other land cover types during the key phenology phase in tropical complex mountainous regions. This method can make full use of the Landsat and MODIS archives as well as their advantages for tropical evergreen forests geospatial inventories, and is simple and easy to use. This method is suggested for use with other similar regions.

On the relationship of NDVI with leaf area index in a deciduous forest site

Vegetation indices serve as an essential tool in monitoring variations in vegetation. The vegetation indices used often, viz., normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) were computed from MODIS vegetation index products. The present study aimed to monitor vegetation's seasonal dynamics by using time series NDVI and EVI indices in Tamil Nadu from 2011 to 2021. Two products characterize the global range of vegetation states and processes more effectively. The data sources were processed and the values of NDVI and EVI were extracted using ArcGIS software. There was a significant difference in vegetation intensity and status of vegetation over time, with NDVI having a larger value than EVI, indicating that biomass intensity varies over time in Tamil Nadu. Among the land cover classes, the deciduous forest showed the highest mean values for NDVI (0.83) and EVI (0.38), followed by cropland mean values of NDVI (0.71) and EVI (0.31) and the lowest NDVI (0.68) and EVI (0.29) was recorded in the scrubland. The study demonstrated that vegetation indices extracted from MODIS offered valuable information on vegetation status and condition at a short temporal time period.

The Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) are vegetation indices widely used in remote sensing of above-ground biomass. Because both indexes are based on spectral features of plant canopy, NDVI and EVI may suffer reduced accuracy in estimating above-ground biomass when flower signals are mixed in the plant canopy. This paper addresses how flowers influence the estimation of above-ground biomass using NDVI and EVI for an alpine meadow with mixed yellow flowers of Halerpestes tricuspis (Ranunculaceae). Field spectral measurements were used in combination with simulated reflectance spectra with precisely controlled flower coverage by applying a linear spectral mixture model. Using the reflectance spectrum for the in-situ canopy with H. tricuspis flowers, we found no significant correlation between above-ground biomass and EVI (p = 0.17) or between above-ground biomass and NDVI (p = 0.78). However, both NDVI and EVI showed very good prediction of above-ground biomass with low root mean square errors (RMSE = 43 g m−2 for NDVI and RMSE = 43 g m−2 for EVI, both p < 0.01) when all the flowers were removed from the canopies. Simulation analysis based on the in-situ measurements further indicated that high variation in flower coverage among different quadrats could produce more noise in the relationship between above-ground biomass and NDVI, or EVI, which results in an evident decline in the accuracy of above-ground biomass estimation. Therefore, the study suggests that attention should be paid both to the flower fraction and the heterogeneity of flower distribution in the above-ground biomass estimation via NDVI and EVI.

The aim of this study is to examine the effects of vegetation indices, Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) on dust storms over Iraq in spring season during the period from 2005 to 2014. NDVI and EVI from MODIS sensor and historical dust storms for 11 stations from Iraqi Meteorological Organization and Seismology are used. The results show that the high spring cumulative sum of dust storm frequency is at Karbala and its lower value at Mosul. Dust storms frequency is decrease with increasing vegetation cover where high dust storm years was in 2008 and 2012 tend to face lower spring vegetation indices values, whereas low frequency of dust storm years 2006 and 2014 for almost selected stations are more likely to have high spring EVI and NDVI values. Barren areas (EVI and NDVI <0.1) and poor vegetation cover (EVI and NDVI <0.2) in large areas of Iraq over nearly 60% of Iraq area exist in west and south of Iraq consider one the important factors for the frequent occurrence of dust storms. NDVI shows good negative correlation with dust storms frequency than EVI, conclude that NDVI is suitable than EVI for dust storm study.

Evaluating impacts of snow, surface water, soil and vegetation on empirical vegetation and snow indices for the Utqiaġvik tundra ecosystem in Alaska with the LVS3 model

The Himalayas constitute one of the richest and most diverse ecosystems in the Indian sub-continent. Vegetation greenness driven by climate in the Himalayan region is often overlooked as field-based studies are challenging due to high altitude and complex topography. Although the basic information about vegetation cover and its interactions with different hydroclimatic factors is vital, limited attention has been given to understanding the response of vegetation to different climatic factors. The main aim of the present study is to analyse the relationship between the spatiotemporal variability of vegetation greenness and associated climatic and hydrological drivers within the Upper Khoh River (UKR) Basin of the Himalayas at annual and seasonal scales. We analysed two vegetation indices, namely, normalised difference vegetation index (NDVI) and enhanced vegetation index (EVI) time-series data, for the last 20 years (2001–2020) using Google Earth Engine. We found that both the NDVI and EVI showed increasing trends in the vegetation greening during the period under consideration, with the NDVI being consistently higher than the EVI. The mean NDVI and EVI increased from 0.54 and 0.31 (2001), respectively, to 0.65 and 0.36 (2020). Further, the EVI tends to correlate better with the different hydroclimatic factors in comparison to the NDVI. The EVI is strongly correlated with ET with r2 = 0.73 whereas the NDVI showed satisfactory performance with r2 = 0.45. On the other hand, the relationship between the EVI and precipitation yielded r2 = 0.34, whereas there was no relationship was observed between the NDVI and precipitation. These findings show that there exists a strong correlation between the EVI and hydroclimatic factors, which shows that changes in vegetation phenology can be better captured using the EVI than the NDVI.

The Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) have gained considerable attention in ecological research and management as proxies for landscape-scale vegetation quantity and quality. In the Greater Yellowstone Ecosystem (GYE), these indices are especially important for mapping spatiotemporal variation in the forage available to migratory elk (Cervus elaphus). Here, we examined how the accuracy of using MODIS-derived NDVI and EVI as proxies for forage biomass and quality differed across elevation-related phenology and land use gradients, determined if polynomial NDVI/EVI, site, and season effects improved these models, and then mapped spatiotemporal variation in the abundance of high quality forage available to elk across the Upper Yellowstone River Basin (UYRB) of the GYE. Models with a polynomial NDVI effect explained 19%–55% more variation in biomass than the linear NDVI and EVI models. Models with linear season effect explained 14%–20% more variation in chlorophyll, 37%–69% more variation in crude protein, and 26%–50% more variation in in vitro dry matter digestibility (IVDMD) than the linear NDVI and EVI models. Linear NDVI models explained more variation in biomass and quality across the UYRB than the linear EVI models. The accuracy of these models was lowest in grasslands with late onset of growth, in irrigated agriculture, and after the peak in biomass. Forage biomass and quality varied across the elevation-related phenology and land use gradients in the UYRB throughout the season. At their seasonal peak, the abundance of high quality forage for elk was 50% greater in grasslands with late onset of growth and 200% greater in irrigated agriculture than in all other grasslands, suggesting that these grasslands play an especially important role in the movement and fitness of migratory elk. These results provide novel information on the utility of NDVI and EVI for mapping spatiotemporal patterns of forage biomass and quality.

Vegetation indices (VIs) such as the Normalized Difference Vegetation Index (NDVI) are widely used for assessing vegetation cover and condition. One of the NDVI's significant disadvantages is its sensitivity to aerosols in the atmosphere, hence several atmospherically resistant VIs were formulated using the difference in the radiance between the blue and the red spectral bands. The state‐of‐the‐art atmospherically resistant VI, which is a standard Moderate Resolution Imaging Spectroradiometer (MODIS) product, together with the NDVI, is the Enhanced Vegetation Index (EVI). A different approach introduced the Aerosol‐free Vegetation Index (AFRI) that is based on the correlation between the shortwave infrared (SWIR) and the visible red bands. The AFRI main advantage is in penetrating an opaque atmosphere influenced by biomass burning smoke, without the need for explicit correction for the aerosol effect. The objective of this research was to compare the performance of these three VIs under smoke conditions. The AFRI was applied to the 2.1 µm SWIR channel of the MODIS sensor onboard the Earth Observing System (EOS) Terra and Aqua satellites in order to assess its functionality on these imaging platforms. The AFRI performance was compared with those of NDVI and EVI. All VIs were calculated on images with and without present smoke, using the surface‐reflectance MODIS product, for three case studies of fires in Arizona, California, and Zambia. The MODIS Fire Product was embedded on the images in order to identify the exact location of the active fires. Although good correlations were observed between all VIs in the absence of smoke (in the Arizona case R 2 = 0.86, 0.77, 0.88 for the NDVI–EVI, AFRI–EVI, and AFRI–NDVI, respectively) under smoke conditions a high correlation was maintained between the NDVI and the EVI, while low correlations were found for the AFRI–EVI and AFRI–NDVI (0.21 and 0.16, for the Arizona case, respectively). A time series of MODIS images recorded over Zambia during the summer of 2000 was tested and showed high NDVI fluctuations during the study period due to oscillations in aerosol optical thickness values despite application of aerosol corrections on the images. In contrast, the AFRI showed smoother variations and managed to better assess the vegetation condition. It is concluded that, beneath the biomass burning smoke, the AFRI is more effective than the EVI in observing the vegetation conditions.

Remote sensing methods are essential for environmental monitoring and assessment. They provide objective data on vegetation cover, enabling the de­tection of changes and evaluation of ecological processes. In Ukraine, located in a temperate climate zone, favorable conditions support vegetation growth and agricultural activities. However, the agricultural sector faces significant environmental challenges, such as climate change, soil degradation, and erosion. Monitoring these processes is crucial for sustainable land use and resource management. To assess changes in vegetation cover and their environmental impact, satellite remote sensing and spectral indices are commonly applied. Widely used indices include the Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI), and their modifications. These indices, however, have limitations, including sensitivity to soil background, atmospheric interferen­ce, and saturation at high vegetation densities. To address these limitations, a new index called EVI-S (Enhanced Vegetation Index — Soil Adjusted) has been proposed. It combines the strengths of SAVI and EVI2 while minimizing atmospheric interference. The primary goal is to develop and validate this new index to enhance ecological monitoring. The study area is the Feodosiivska territorial community in Kyiv region, which includes natural ecosystems and agri­cultural lands. The landscape is predominantly flat, typical of the forest-steppe zone. Sentinel-2 satel­lite images, processed using QGIS with the SCP plugin, provided data for calculating NDVI, EVI, EVI2, SAVI, and EVI-S indices. Results indicate that the EVI-S index shows higher maximum and average values compared to conventional indices, suggesting improved sensitivity to dense vegetation. Correlation analysis demonstrated that while traditional indices (EVI, EVI2, SAVI) show strong mutual correlation, NDVI displays slightly lower correlation due to satu­ration issues. Notably, EVI-S exhibits a high correla­tion with NDVI, indicating retained similarity despite improved formulation. EVI-S is particularly effec­tive in analyzing urban and mixed landscapes, where conventional indices may underestimate vegetation presence. Its wider dynamic range and increased sensitivity make it useful for monitoring vegetation growth and biomass, especially in forests and agri­cultural areas. Further studies are needed to assess its application in urban environments, where hetero­ geneous vegetation cover may impact accuracy. The findings demonstrate that EVI-S can complement traditional indices, offering enhanced sensitivity in contexts where accurate vegetation density analysis is necessary. Its practical application is promising for crop monitoring during growth periods and for evaluating dense forest areas.

Drought poses a significant environmental risk and can deeply affect the growth of grasslands. However, there is still uncertainty regarding the precise impact of varying levels of drought on grassland growth. To address this gap, we utilized several key indicators, including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Global Orbiting Carbon Observatory-2-based Solar-induced Chlorophyll Fluorescence (GOSIF), and Gross Primary Productivity (GPP), in conjunction with drought indices (the Standardized Precipitation Evapotranspiration Index (SPEI) and soil moisture (SM). Our study aimed to comprehensively assess the consistency of spatiotemporal patterns in grassland vegetation and its responsiveness to different drought levels in the Inner Mongolia region from 2002 to 2020. The results indicated that NDVI, EVI, GOSIF, and GPP in grassland vegetation across Inner Mongolia exhibited significant increasing trends from 2002 to 2020. Specifically, NDVI, EVI, GOSIF, and GPP all displayed consistent spatial patterns, with 25.83%, 21.18%, 22.65%, and 48.13% of the grassland area showing significant increases, respectively. Drought events, as described through SPEI and SM, from June 2007 to September 2007 and June 2017 to July 2017 were selected to evaluate the response of grassland vegetation to drought. The drought events of 2007 and 2017 resulted in reductions in NDVI, EVI, GOSIF, and GPP relative to the multi-year average (2002–2020). GOSIF exhibited a more intense response to drought, suggesting that GOSIF may reflect the inhibition of water stress on grassland photosynthesis better than NDVI and EVI for the drought in 2007 and 2017. The reductions in NDVI, EVI, GOSIF, and GPP in grassland increased significantly across different drought levels, with the sharpest reductions observed during extreme drought. Under the severe and extreme drought events, the most substantial reductions in NDVI, EVI, GOSIF, and GPP were observed in the temperate steppe (TS). Moreover, the effects of different drought severity levels within the same grassland type varied, with the most significant reductions in NDVI, EVI, GOSIF, and GPP observed during extreme drought. Our results provide new perspectives for developing and implementing effective strategies to address grassland carbon cycling management and climate change in Inner Mongolia.