Evaluation and Normalization of Topographic Effects on Vegetation Indices

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.

Vegetation indices play an important role in monitoring variations in vegetation. The Enhanced Vegetation Index (EVI) proposed by the MODIS Land Discipline Group and the Normalized Difference Vegetation Index (NDVI) are both global-based vegetation indices aimed at providing consistent spatial and temporal information regarding global vegetation. However, many environmental factors such as atmospheric conditions and soil background may produce errors in these indices. The topographic effect is another very important factor, especially when the indices are used in areas of rough terrain. In this paper, we theoretically analyzed differences in the topographic effect on the EVI and the NDVI based on a non-Lambertian model and two airborne-based images acquired from a mountainous area covered by high-density Japanese cypress plantation were used as a case study. The results indicate that the soil adjustment factor “L” in the EVI makes it more sensitive to topographic conditions than is the NDVI. Based on these results, we strongly recommend that the topographic effect should be removed in the reflectance data before the EVI was calculated—as well as from other vegetation indices that similarly include a term without a band ratio format (e.g., the PVI and SAVI)—when these indices are used in the area of rough terrain, where the topographic effect on the vegetation indices having only a band ratio format (e.g., the NDVI) can usually be ignored.

The objective of the current study was the investigation of specific relationships between crop coefficients and vegetation indices (VI) computed at the water-limited environment of Lake Karla Watershed, Thessaly, in central Greece. A Mapping ET (evapotranspiration) at high Resolution and with Internalized Calibration (METRIC) model was used to derive crop coefficient values during the growing season of 2012. The proposed methodology was developed using medium resolution Landsat 7 ETM+ images and meteorological data from a local weather station. Cotton, sugar beets, and corn fields were utilized. During the same period, spectral signatures were obtained for each crop using the field spectroradiometer GER1500 (Spectra Vista Corporation, NY, U.S.A.). Relative spectral responses (RSR) were used for the filtering of the specific reflectance values giving the opportunity to match the spectral measurements with Landsat ETM+ bands. Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI) and Enhanced Vegetation Index 2 (EVI2) were then computed, and empirical relationships were derived using linear regression analysis. NDVI, SAVI, and EVI2 were tested separately for each crop. The resulting equations explained those relationships with a very high R2 value (>0.86). These relationships have been validated against independent data. Validation using a new image file after the experimental period gives promising results, since the modeled image file is similar in appearance to the initial one, especially when a crop mask is applied. The CROPWAT model supports those results when using the new crop coefficients to estimate the related crop water requirements. The main benefit of the new approach is that the derived relationships are better adjusted to the crops. The described approach is also less time-consuming because there is no need for atmospheric correction when working with ground spectral measurements.

In the current study area (Faridabad, Gurugram, Ghaziabad, and Gautam Buddha Nagar), the aerosol concentration is very high, adversely affecting the environmental conditions and air quality. Investigating the impact of Land Use Land Cover (LULC) on Aerosol Optical Depth (AOD) helps us to develop effective solutions for improving air quality. Hence, the spectral indices derived from LULC ((Normalized difference vegetation index (NDVI), Soil adjusted vegetation index (SAVI), Enhanced vegetation index (EVI), and Normalized difference build-up index (NDBI)) with Moderate Resolution Imaging Spectroradiometer (MODIS) Multiangle Implementation of Atmospheric Correction (MAIAC) high spatial resolution (1 km) AOD from the years 2010–2019 (less to high urbanized period) has been correlated. The current study used remote sensing and Geographical Information System (GIS) techniques to examine changes in LULC in the current study region over the ten years (2010–2019) and the relationship between LULC and AOD. A significant increase in built-up areas (12.18%) and grasslands (51.29%) was observed during 2010–2019, while cropland decreased by 4.42%. A positive correlation between NDBI and SAVI (0.35, 0.27) indicates that built-up soils play an important role in accumulating AOD in a semi-arid region. At the same time, a negative correlation between NDVI and EVI (−0.24, −0.15) indicates the removal of aerosols due to an increase in vegetation. The results indicate that SAVI can play an important role in PM2.5 modeling in semi-arid regions. Based on these findings, urban planners can improve land use management, air quality, and urban planning.

An error and sensitivity analysis of atmospheric resistant vegetation indices derived from dark target-based atmospheric correction

The present study assesses the temporal dynamics of above ground biomass (AGB) in Kaimoor Wildlife Sanctuary (KWS), Uttar Pradesh, India using series of Landsat data for the years 1989, 2000, 2010 and 2018. The satellite images were preprocessed for surface reflectance, and subsequently we computed Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Enhanced Vegetation Index (EVI). A forest density map was generated by slicing the computed NDVI and this was used as a basis for sampling strategy. A total of 30 sampling locations were randomly identified. Plot size of 30 m × 30 m was established. Tree species were enumerated at each plot and the girth at breast height and tree height measurements were recorded for the year 2018. Tree and plot level AGB (i.e. AGBF, t ha−1) was computed by multiplying the tree volume and specific gravity of wood. AGB (i.e. AGBP) prediction models were developed as linear regression equations for the year 2018 by assessing the vegetation indices and the AGBF. The significant AGBP models (R2 = 0.94; P = 0.0001) were applied for all study years after the data correction among the Landsat sensor series. The AGBP was over estimated (22.67 t ha−1; 7.75%) compared to AGBF (t ha−1). Moreover, EVI (R2 = 0.90) was found to be a better predictor for AGB compared to NDVI (R2 = 0.69) or SAVI (R2 = 0.77). The AGBP of KWS ranged between 289 ± 36 (in 1989) and 292 ± 40 (in 2018) with an average decadal positive change of 1.06%.

Development of a two-band enhanced vegetation index without a blue band

Assessing crop yield trends over years is a key step in site specific management, in view of improving the economic and environmental profile of agriculture. This study was conducted in a 11.07 ha area under Mediterranean climate in Northern Italy to evaluate the spatial variability and the relationships between six remotely sensed vegetation indices (VIs) and grain yield (GY) in five consecutive years. A total of 25 satellite (Landsat 5, 7, and 8) images were downloaded during crop growth to obtain the following VIs: Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), Green Normalized Difference Vegetation Index (GNDVI), Green Chlorophyll Index (GCI), and Simple Ratio (SR). The surveyed crops were durum wheat in 2010, sunflower in 2011, bread wheat in 2012 and 2014, and coriander in 2013. Geo-referenced GY and VI data were used to generate spatial trend maps across the experimental field through geostatistical analysis. Crop stages featuring the best correlations between VIs and GY at the same spatial resolution (30 m) were acknowledged as the best periods for GY prediction. Based on this, 2–4 VIs were selected each year, totalling 15 VIs in the five years with r values with GY between 0.729** and 0.935**. SR and NDVI were most frequently chosen (six and four times, respectively) across stages from mid vegetative to mid reproductive growth. Conversely, SAVI never had correlations high enough to be selected. Correspondence analysis between remote VIs and GY based on quantile ranking in the 126 (30 m size) pixels exhibited a final agreement between 64% and 86%. Therefore, Landsat imagery with its spatial and temporal resolution proved a good potential for estimating final GY over different crops in a rotation, at a relatively small field scale.

The enhanced vegetation index (EVI) has been found useful in improving linearity with biophysical vegetation properties and in reducing saturation effects found in densely vegetated surfaces, commonly encountered in the normalized difference vegetation index (NDVI). However, EVI requires a blue band and is sensitive to variations in blue band reflectance, which limits consistency of EVI across different sensors. The objectives of this study are to develop a 2-band EVI (EVI2) without a blue band that has the best similarity with the 3-band EVI, and to investigate the crosssensor continuity of the EVI2 from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR). A linearity-adjustment factor (β) was introduced and coupled with the soil adjustment factor (L) used in the soil-adjusted vegetation index (SAVI) in the development of the EVI2 equation. The similarity between EVI and EVI2 was validated at the global scale. After a linear adjustment, the AVHRR EVI2 was found to be comparable with the MODIS EVI2. The good agreement between the AVHRR and MODIS EVI2 suggests the possibility of extending the current MODIS EVI time series to the historical AVHRR data, providing another longterm vegetation record different from the NDVI counterpart.

Comparative analysis of IKONOS, SPOT, and ETM+ data for leaf area index estimation in temperate coniferous and deciduous forest stands

Estimation of crop gross primary production (GPP): I. impact of MODIS observation footprint and impact of vegetation BRDF characteristics

Dynamics of land cover and land use in Pernambuco (Brazil): Spatio-temporal variability and temporal trends of biophysical parameters

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.

This study investigates the spatio-temporal changes in Ain Abid (Constantine, Algeria) between 2003 and 2023 using multi-index remote sensing analysis. Through Google Earth Engine platform and Landsat imagery (Landsat 5 TM and Landsat 8 OLI/TIRS), we examined land use/land cover changes and vegetation dynamics using three complementary vegetation indices: Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Soil-Adjusted Vegetation Index (SAVI). Results reveal significant landscape transformations, including a marked increase in urban areas from 6.90 km² to 19.00 km², a dramatic 98% reduction in forest cover, and a doubling of agricultural vegetation areas. The correlation analysis between indices showed perfect correlation between NDVI and SAVI (r=1.00), while EVI provided complementary information with distinct correlations (r=0.76 and 0.61). Classification accuracy assessment yielded Kappa coefficients of 89.1% and 92.3% for 2003 and 2023, respectively. These findings highlight concerning trends in environmental degradation and landscape homogenization, emphasizing the urgent need for sustainable land management practices in this semi-arid region. The study provides valuable insights for policymakers and land managers in developing strategies for balanced territorial development and environmental conservation in Ain Abid.

Drought is a transitory anomaly, prolonged, characterised by a period with precipitation values lower than normal in a specific area. The initial cause of any drought is a shortage of precipitation (meteorological drought) which leads to a shortage of water resources (hydrological drought) necessary to supply the existing demand. Flash drought is a critical sub-seasonal phenomenon that can be devasted for the ecosystems and, consequently, for general economy and health. There are areas where droughts are more devastating, and Spain is in a medium risk zone. In addition to water supplies, one of the first elements where the effects of droughts are first felt is on vegetation. Recent studies have addressed the relationship between NDVI and drought events. They concluded that, although vegetation activity over large parts of Spain is closely related to the interannual variability of drought, there are clear seasonal differences in the response of the NDVI to drought.The World Meteorological Organization (WMO) categorises various drought indices into different groups such as (a) meteorology, (b) soil moisture, (c) hydrology, (d) remote sensing and (e) composite or modelled. Within the group of indices that can be defined by remote sensing, it points out some indices as possible predictors or evaluators of drought periods such us the Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI). However, WMO leaves open the use of other possible VIs for drought prediction or assessment. In the current scenario, where there are multiple vegetation indices that can be used from satellite imagery, the initial objective of the study is to establish, from a set of VIs proposed or not by the WMO, which ones have the highest correlation with drought events in the study area. This correlation will be analysed according to vegetation type&amp;#160; (using the categorisation of the recently published ESA World cover map), in order to attempt to determine the behaviour of the vegetation index under meteorology according to each type. The study area is located in the Extremadura region of western Spain. The mean annual precipitation of the zone ranges from 446 to 1323 mm. The precipitations occur mainly from October to April while June, July and August suffer a significant drought with none or close to zero precipitation amount. The land cover types are mainly forests, agricultural and impervious cover. Regarding the temporal extent, two episodes of severe drought (2005-2006 and 2021-2022) will be studied.Firstly, the vegetation indices available in open collections like OpenEO or Copernicus Global Land Service will be used. All available indices will be used to create time series to be compared with meteorological time series. Once the correlation is established, it will be analysed according to the type of coverage of the World cover map, in order to establish which index correlates better with drought episodes and thus try to establish the best predictor/evaluator.