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Journal articles on the topic 'Vegetation indices'

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Published: 4 June 2021
Last updated: 11 June 2025

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1

Maxwald, Melanie, Markus Immitzer, Hans Peter Rauch, and Federico Preti. "Analyzing Fire Severity and Post-Fire Vegetation Recovery in the Temperate Andes Using Earth Observation Data." Fire 5, no. 6 (2022): 211. http://dx.doi.org/10.3390/fire5060211.

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In wildfire areas, earth observation data is used for the development of fire-severity maps or vegetation recovery to select post-fire measures for erosion control and revegetation. Appropriate vegetation indices for post-fire monitoring vary with vegetation type and climate zone. This study aimed to select the best vegetation indices for post-fire vegetation monitoring using remote sensing and classification methods for the temperate zone in southern Ecuador, as well as to analyze the vegetation’s development in different fire severity classes after a wildfire in September 2019. Random forest classification models were calculated using the fire severity classes (from the Relativized Burn Ratio—RBR) as a dependent variable and 23 multitemporal vegetation indices from 10 Sentinel-2 scenes as descriptive variables. The best vegetation indices to monitor post-fire vegetation recovery in the temperate Andes were found to be the Leaf Chlorophyll Content Index (LCCI) and the Normalized Difference Red-Edge and SWIR2 (NDRESWIR). In the first post-fire year, the vegetation had already recovered to a great extent due to vegetation types with a short life cycle (seasonal grass-species). Increasing index values correlated strongly with increasing fire severity class (fire severity class vs. median LCCI: 0.9997; fire severity class vs. median NDRESWIR: 0.9874). After one year, the vegetations’ vitality in low severity and moderate high severity appeared to be at pre-fire level.
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2

Jackson, Ray D., and Alfredo R. Huete. "Interpreting vegetation indices." Preventive Veterinary Medicine 11, no. 3-4 (1991): 185–200. http://dx.doi.org/10.1016/s0167-5877(05)80004-2.

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3

Jinguo, Yuan, and Wang Wei. "Identification of Forest Vegetation Using Vegetation Indices." Chinese Journal of Population Resources and Environment 2, no. 4 (2004): 12–16. http://dx.doi.org/10.1080/10042857.2004.10677383.

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4

DUBEY, R. C., S. D. GAIKWAD, V. S. NAWATHE, R. G. DEKHANE, and S. N. BIDYANTA. "Spectral radiance characteristics and vegetative indices of crops -A ground based remote sensing technique." MAUSAM 46, no. 1 (2022): 75–80. http://dx.doi.org/10.54302/mausam.v46i1.3186.

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The spectral radiance characteristics and vegetation indices like simple difference, ratio vegetation, normalised vegetation perpendicular vegetation transformed vegetation and tasseled cap transformation of mung been sunflower and groundnut crops at different growth stages have been studied. The experiment was conducted in post rainy season during 1990-91 in the farm of Agricultural College. Pune using hand held multi-spectral radiometer. The significance of spectral variation of radiance and vegetative indices with respect to the phenological stages are discussed.
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5

Purevdorj, TS, R. Tateishi, T. Ishiyama, and Y. Honda. "Relationships between percent vegetation cover and vegetation indices." International Journal of Remote Sensing 19, no. 18 (1998): 3519–35. http://dx.doi.org/10.1080/014311698213795.

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6

Bannari, A., D. Morin, F. Bonn, and A. R. Huete. "A review of vegetation indices." Remote Sensing Reviews 13, no. 1-2 (1995): 95–120. http://dx.doi.org/10.1080/02757259509532298.

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7

Wiegand, C. L., A. J. Richardson, D. E. Escobar, and A. H. Gerbermann. "Vegetation indices in crop assessments." Remote Sensing of Environment 35, no. 2-3 (1991): 105–19. http://dx.doi.org/10.1016/0034-4257(91)90004-p.

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8

Villa, Paolo, Mariano Bresciani, Federica Braga, and Rossano Bolpagni. "Comparative Assessment of Broadband Vegetation Indices Over Aquatic Vegetation." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7, no. 7 (2014): 3117–27. http://dx.doi.org/10.1109/jstars.2014.2315718.

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9

ORMSBY, J. P., B. J. CHOUDHURY, and M. OWE. "Vegetation spatial variability and its effect on vegetation indices." International Journal of Remote Sensing 8, no. 9 (1987): 1301–6. http://dx.doi.org/10.1080/01431168708954775.

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10

Jafari, R., M. M. Lewis, and B. Ostendorf. "Evaluation of vegetation indices for assessing vegetation cover in southern arid lands in South Australia." Rangeland Journal 29, no. 1 (2007): 39. http://dx.doi.org/10.1071/rj06033.

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Vegetation indices are widely used for assessing and monitoring ecological variables such as vegetation cover, above-ground biomass and leaf area index. This study reviewed and evaluated different groups of vegetation indices for estimating vegetation cover in southern rangelands in South Australia. Slope-based, distance-based, orthogonal transformation and plant-water sensitive vegetation indices were calculated from Landsat thematic mapper (TM) image data and compared with vegetation cover estimates at monitoring points made during Pastoral Lease assessments. Relationships between various vegetation indices and vegetation cover were compared using simple linear regression at two different scales: within two contrasting land systems and across broader regional landscapes. Of the vegetation indices evaluated, stress related vegetation indices using red, near-infrared and mid-infrared TM bands consistently showed significant relationships with vegetation cover at both land system and landscape scales. Estimation of vegetation cover was more accurate within land systems than across broader regions. Total perennial and ephemeral plant cover was best predicted within land systems, while combined vegetation, plant litter and soil cryptogam crust cover was best predicted at landscape scale. These results provide a strong foundation for use of vegetation indices as an adjunct to field methods for assessing vegetation cover in southern Australia.
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11

Radočaj, Dorijan, Ante Šiljeg, Rajko Marinović, and Mladen Jurišić. "State of Major Vegetation Indices in Precision Agriculture Studies Indexed in Web of Science: A Review." Agriculture 13, no. 3 (2023): 707. http://dx.doi.org/10.3390/agriculture13030707.

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Vegetation indices provide information for various precision-agriculture practices, by providing quantitative data about crop growth and health. To provide a concise and up-to-date review of vegetation indices in precision agriculture, this study focused on the major vegetation indices with the criterion of their frequency in scientific papers indexed in the Web of Science Core Collection (WoSCC) since 2000. Based on the scientific papers with the topic of “precision agriculture” combined with “vegetation index”, this study found that the United States and China are global leaders in total precision-agriculture research and the application of vegetation indices, while the analysis adjusted for the country area showed much more homogenous global development of vegetation indices in precision agriculture. Among these studies, vegetation indices based on the multispectral sensor are much more frequently adopted in scientific studies than their low-cost alternatives based on the RGB sensor. The normalized difference vegetation index (NDVI) was determined as the dominant vegetation index, with a total of 2200 studies since the year 2000. With the existence of vegetation indices that improved the shortcomings of NDVI, such as enhanced vegetation index (EVI) and soil-adjusted vegetation index (SAVI), this study recognized their potential for enabling superior results to those of NDVI in future studies.
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12

Eastwood, J. A., M. G. Yates, A. G. Thomson, and R. M. Fuller. "The reliability of vegetation indices for monitoring saltmarsh vegetation cover." International Journal of Remote Sensing 18, no. 18 (1997): 3901–7. http://dx.doi.org/10.1080/014311697216739.

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13

Pei, Fengsong, Changjiang Wu, Xiaoping Liu, et al. "Monitoring the vegetation activity in China using vegetation health indices." Agricultural and Forest Meteorology 248 (January 2018): 215–27. http://dx.doi.org/10.1016/j.agrformet.2017.10.001.

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14

Mustapha Baba, Bashariya, Zaharaddeen Isa, Auwal Faruq Abdussalam, and Abu-Hanifa Babati. "INFLUENCE OF CLIMATE INDICES ON VEGETATION DYNAMICS IN KAMUKU NATIONAL PARK, NIGERIA USING COUPLED MODEL INTERCOMPARISON PROJECT PHASE 6 (CMIP6)." FUDMA JOURNAL OF SCIENCES 6, no. 4 (2022): 160–73. http://dx.doi.org/10.33003/fjs-2022-0604-1059.

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This study examined the influence of climate indices on vegetation dynamics in Kamuku national park, Nigeria. MODIS NDVI dataset was obtained from 2000 – 2021, while temperature and rainfall data were obtained from NiMet Kaduna international airport from 1980 - 2015. Downscale climate indices from Six-generation (Coupled Model Intercomparison Project Phase 6 (CMIP 6)) global climate model was obtained from Copernicus from 1850 - 2099. Coefficient of variability, Mann Kendall and correlation were used to examine the variability, trend and relationship. Subsequently, multiple linear regression was used to assess the influence of climate indices on vegetation dynamics. The result revealed that rainfall indices have moderate variability and temperature indices show a low variability, while the normalized vegetation index revealed a low variability of vegetation vigour in the study area. In addition, there is a weak positive relationship between the rainfall indices and vegetation and a negative relationship between temperature indices and the vegetation vigour. The climate indices were able to explain 47 % (R2 = 0.47) variance of the vegetation vigour in Kamuku national park. While the remaining 53% might be a result of other factors such as human activities and other environmental factors. In conclusion, the vegetation vigour regulates the distribution of the climate extreme indices and might likely be more influenced by the human activities
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15

Alvino, Francisco C. G., Catariny C. Aleman, Roberto Filgueiras, Daniel Althoff, and Fernando F. da Cunha. "VEGETATION INDICES FOR IRRIGATED CORN MONITORING." Engenharia Agrícola 40, no. 3 (2020): 322–33. http://dx.doi.org/10.1590/1809-4430-eng.agric.v40n3p322-333/2020.

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16

Johnson, Brian. "Effects of Pansharpening on Vegetation Indices." ISPRS International Journal of Geo-Information 3, no. 2 (2014): 507–22. http://dx.doi.org/10.3390/ijgi3020507.

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17

Yanev, Toni K. "Probability density functions of vegetation indices." Acta Astronautica 26, no. 2 (1992): 85–91. http://dx.doi.org/10.1016/0094-5765(92)90049-o.

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18

Myneni, R. B., and G. Asrar. "Atmospheric effects and spectral vegetation indices." Remote Sensing of Environment 47, no. 3 (1994): 390–402. http://dx.doi.org/10.1016/0034-4257(94)90106-6.

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19

Rondeaux, Geneviève, Michael Steven, and Frédéric Baret. "Optimization of soil-adjusted vegetation indices." Remote Sensing of Environment 55, no. 2 (1996): 95–107. http://dx.doi.org/10.1016/0034-4257(95)00186-7.

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20

Beniaich, Adnane, Marx Leandro Naves Silva, Fabio Arnaldo Pomar Avalos, Michele Duarte de Menezes, and Bernardo Moreira Cândido. "Determination of vegetation cover index under different soil management systems of cover plants by using an unmanned aerial vehicle with an onboard digital photographic camera." Semina: Ciências Agrárias 40, no. 1 (2019): 49. http://dx.doi.org/10.5433/1679-0359.2019v40n1p49.

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The permanent monitoring of vegetation cover is important to guarantee a sustainable management of agricultural activities, with a relevant role in the reduction of water erosion. This monitoring can be carried out through different indicators such as vegetation cover indices. In this study, the vegetation cover index was obtained using uncalibrated RGB images generated from a digital photographic camera on an unmanned aerial vehicle (UAV). In addition, a comparative study with 11 vegetation indices was carried out. The vegetation indices CIVE and EXG presented a better performance and the index WI presented the worst performance in the vegetation classification during the cycles of jack bean and millet, according to the overall accuracy and Kappa coefficient. Vegetation indices were effective tools in obtaining soil cover index when compared to the standard Stocking method, except for the index WI. Architecture and cycle of millet and jack bean influenced the behavior of the studied vegetation indices. Vegetation indices generated from RGB images obtained by UAV were more practical and efficient, allowing a more frequent monitoring and in a wider area during the crop cycle.
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21

SAITO, Atsushi, and Takeshi YAMAZAKI. "Characteristics of Spectral Reflectance for Vegetation Ground Surfaces with Snow-cover; Vegetation Indices and Snow Indices." Journal of Japan Society of Hydrology and Water Resources 12, no. 1 (1999): 28–38. http://dx.doi.org/10.3178/jjshwr.12.28.

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22

Wójcik-Gront, Elżbieta, Dariusz Gozdowski, and Wojciech Stępień. "UAV-Derived Spectral Indices for the Evaluation of the Condition of Rye in Long-Term Field Experiments." Agriculture 12, no. 10 (2022): 1671. http://dx.doi.org/10.3390/agriculture12101671.

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The aim of the study was to evaluate the effect of various fertilization treatments, including nitrogen, potassium and phosphorus fertilization, in long-term experiments for selected UAV (unmanned aerial vehicle)-derived spectral vegetation indices (NDVI—Normalized Difference Vegetation Index, NDRE—Normalized Difference Red Edge Index, VARI—Visible Atmospherically Resistant Index, TGI—Triangular Greenness Index, SIPI2—Structure Insensitive Pigment Index 2, LCI—Leaf Chlorophyll Index, BNDVI—Blue Normalized Difference Vegetation Index, GNDVI—Green Normalized Difference Vegetation Index, MCARI—Modified Chlorophyll Absorption in Reflective Index) based on multispectral (bands in the range of visible light and near infra-red) images of winter rye. The strongest effect on the studied vegetation indices was nitrogen fertilization, which discriminated values of most of the vegetation indices. The effect of phosphorus and potassium fertilization on the studied vegetation indices was much weaker. The treatments with nitrogen fertilization had significantly higher values of most vegetation indices in comparison to treatments without nitrogen. This was confirmed by principal component analysis (PCA), in which treatments without nitrogen fertilization were very different in comparison to all other treatments where nitrogen fertilization was applied. The effect of phosphorus and potassium fertilization on most of vegetation indices was relatively weak and not significant in most experiments. Only for rye cultivated in monoculture was the effect of phosphorus fertilization significant for most of vegetation indices in early growth stages. In later growth stages (heading and flowering) the effect of phosphorus fertilization was significant in rye monoculture for the SIPI2 vegetation index. Mean SIPI2 was higher for the fertilization treatment CaNPK in comparison to CaKN (without P fertilization). The effect of potassium fertilization on the studied vegetation indices was very weak, and in most cases not significant. The effect of nitrogen fertilization on vegetation indices was much stronger than effect of both potassium and phosphorus fertilization.
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23

Dikici, Mehmet. "Drought Analysis for the Seyhan Basin with Vegetation Indices and Comparison with Meteorological Different Indices." Sustainability 14, no. 8 (2022): 4464. http://dx.doi.org/10.3390/su14084464.

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Various drought indices have been developed to monitor drought, which is a result of climate change, and mitigate its adverse effects on water resources, especially in agriculture. Vegetation indices determined by remote sensing were examined by many recent studies and shed light on drought risk management. In the current study, one of the 25 drainage basins in Turkey—the Seyhan Basin, located in the south of the country—was investigated. The Normalized Difference Vegetation Index (NDVI) and the Vegetation Condition Index (VCI) are the most widely used vegetation indices and are very useful because they give results only based on satellite images. This study examined the Seyhan Basin using satellite data in which the vegetation transformation occurring due to the decline of agricultural and forest areas was seen. An increase in drought frequency was detected in the Seyhan Basin using the NDVI and VCI indices and compared with different indices. The results obtained revealed that climate change and drought is increasing with a linear uptrend. It is recommended that decision-makers take the necessary measures by considering the drought risk maps. Long-term drought management plans should also be prepared and implemented.
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24

Modzelewska, Aneta, Krzysztof Stereńczak, Monika Mierczyk, Sylwia Maciuk, Radomir Bałazy, and Tomasz Zawiła-Niedźwiecki. "Sensitivity of vegetation indices in relation to parameters of Norway spruce stands." Folia Forestalia Polonica 59, no. 2 (2017): 85–98. http://dx.doi.org/10.1515/ffp-2017-0009.

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AbstractThe main goal of this research is to shed further light on the sensitivity of the vegetation indices to spatial changes of stand parameters. The analysis was done within mountain forests in the Sudetes and the Beskids in southern Poland. Some 1327 stands were analysed with more than 70 percent of spruce contribution in the species composition. The response of selected vegetation indices was verified in relation to the alterations of spruce participation, stand height, volume, stand density and diameter. The following indices were analysed: Normalized Difference Vegetation Index, Normalized Difference Red Edge Index, Green Normalized Difference Vegetation Index and Wide Dynamic Range Vegetation Index. Indices were calculated based on the Rapid Eye (Black Bridge) images. All the analysed stand characteristics influence the values of vegetation indices. In general: mean height, diameter at breast height, volume and spruce participation are the most negatively correlated with the indices. Density is a variable that, in general, cannot directly be used for indices correction, because it is hard to find any stable trend. NDRE is the most stable index for the analysis of stand characteristics.
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Akumu, Clement E., and Eze O. Amadi. "Examining the Integration of Landsat Operational Land Imager with Sentinel-1 and Vegetation Indices in Mapping Southern Yellow Pines (Loblolly, Shortleaf, and Virginia Pines)." Photogrammetric Engineering & Remote Sensing 88, no. 1 (2022): 29–38. http://dx.doi.org/10.14358/pers.21-00024r2.

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The mapping of southern yellow pines (loblolly, shortleaf, and Virginia pines) is important to supporting forest inventory and the management of forest resources. The overall aim of this study was to examine the integration of Landsat Operational Land Imager (OLI ) optical data with Sentinel-1 microwave C-band satellite data and vegetation indices in mapping the canopy cover of southern yellow pines. Specifically, this study assessed the overall mapping accuracies of the canopy cover classification of southern yellow pines derived using four data-integration scenarios: Landsat OLI alone; Landsat OLI and Sentinel-1; Landsat OLI with vegetation indices derived from satellite data—normalized difference vegetation index, soil-adjusted vegetation index, modified soil-adjusted vegetation index, transformed soil-adjusted vegetation index, and infrared percentage vegetation index; and 4) Landsat OLI with Sentinel-1 and vegetation indices. The results showed that the integration of Landsat OLI reflectance bands with Sentinel-1 backscattering coefficients and vegetation indices yielded the best overall classification accuracy, about 77%, and standalone Landsat OLI the weakest accuracy, approximately 67%. The findings in this study demonstrate that the addition of backscattering coefficients from Sentinel-1 and vegetation indices positively contributed to the mapping of southern yellow pines.
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26

Merrill, Eveyln, and Ronald Marrs. "Remote Sensing of Vegetation Recovery in Grasslands after the 1988 Fires in Yellowstone National Park." UW National Parks Service Research Station Annual Reports 17 (January 1, 1993): 130–38. http://dx.doi.org/10.13001/uwnpsrc.1993.3169.

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Traditional methods for measurement of vegetative characteristics can be time-consuming and labor-intensive, especially across large areas. Yet such estimates are necessary to investigate the effects of large scale disturbances on ecosystem components and processes. Because foliage of plants differentially absorbs and reflects energy within the electromagnetic spectrum, one alternative for monitoring vegetation is to use remotely sensed spectral data (Tueller 1989). Spectral indices developed from field radiometric and Landsat data have been used successfully to quantify green leaf area, biomass, and total yields in relatively homogeneous fields for agronomic uses (Shibayama and Akiyama 1989}, but have met with variable success in wildland situations (Pearson et al. 1976). Interference from soils (Hardinsky et al. 1984, Huete et al. 1985), weathered litter (Huete and Jackson 1987), and senesced vegetation (Sellers 1985) have diminished the relationship between green vegetation characteristics and various vegetation indices.
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27

Bagheri, N., H. Ahmadi, S. Alavipanah, and M. Omid. "Soil-line vegetation indices for corn nitrogen content prediction." International Agrophysics 26, no. 2 (2012): 103–8. http://dx.doi.org/10.2478/v10247-012-0016-8.

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Soil-line vegetation indices for corn nitrogen content prediction The soil-line vegetation indices for prediction of corn canopy nitrogen content were investigated. Results indicated that the vegetation indices applied were correlated with corn canopy nitrogen content and the wavelengths between 630-860 nm are suitable for nitrogen diagnosis. The second-order polynomial equation was the best model for nitrogen content prediction among different regression types. Analyses based on both predicted and measured data were carried out to compare the performance of existing vegetation indices.
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28

Liu, Liang, Jing Jing Zhang, Ming Jiang Zhang, Wan Guo Wang, Chuan Hu Wei, and Tian Ru Zheng. "Spatial Mapping of Vegetation Based on Out-of-Box Vegetation Indices." Applied Mechanics and Materials 644-650 (September 2014): 4657–62. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.4657.

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The infrastructures of power grid can be easily damaged by fire disasters. Monitoring the spatial variations in the vegetation fraction is significant to predicate the probability of a fire disaster. While each vegetation indices (VI) computing method tends to suit some problem better than others, and typically have different parameters and configurations to be adjusted before achieving optimal performance on a dataset, we focus on conjunction with many types of traditional VIs' computing methods to improve their performance. Experimental results show that the accuracy of vegetation fraction estimation based on our method is higher than by using any single vegetation indices.
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29

Rees, W. G., E. I. Golubeva, and M. Williams. "Are vegetation indices useful in the Arctic?" Polar Record 34, no. 191 (1998): 333–36. http://dx.doi.org/10.1017/s0032247400026036.

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AbstractThis paper describes a preliminary investigation of the extent to which the normalised difference vegetation index (NDVI), derived from satellite optical imagery, can indicate the extent of damage to upland tundra (fruticose lichen and dwarf shrub) vegetation. We combine the results of a previously reported classification of Landsat multispectral scanner imagery from Kol'skiy Poluostrov, Russia, with field measurements of the biomass and spectral reflectance of tundra vegetation. The results show that the NDVI is not strongly influenced by biomass, but that differences in species composition and ground cover are significant. Other workers have concluded that vegetation indices are not useful for boreal forests. It is therefore suggested that the use of the NDVI by itself as an indicator of the state of disturbed vegetation in Arctic regions is not recommended.
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30

Hidayati, Iswari Nur, R. Suharyadi, and Projo Danoedoro. "Exploring Spectral Index Band and Vegetation Indices for Estimating Vegetation Area." Indonesian Journal of Geography 50, no. 2 (2018): 211. http://dx.doi.org/10.22146/ijg.38981.

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Visual analysis and transformation of vegetation indices have been widely applied in studies of vegetation density using remote sensing data. However, visual analysis is time intensive compared to index transformation. On the other hand, the index transformation from medium resolution imagery is not fully representative for urban vegetation studies. Meanwhile, the spectral range of high-resolution imagery is usually limited to visible wavelengths for the image transformation. Worldview-2 imagery provides a new breakthrough with a high spatial resolution and supports various spectral resolutions. This study aims to explore the spectral value of the Worldview-2 image index for estimation of vegetation density. Normalized indices were made for 56 band combinations and Otsu thresholding was implemented for the threshold selection to separate vegetation and non-vegetation areas. This thresholding was done by minimizing classes’ variances between two groups of pixels which are distinguished by system or classification. The image binarization process was performed to differentiate between vegetation and non-vegetation. For the accuracy testing, a total of 250 samples was produced by a stratified random sampling method. Our results show that the combination of indices from red channel, red-edge, NIR-1, and NIR-2 provides the best accuracy for semantic accuracy. Vegetation area extracted from the index was then compared with the results of the visual analysis. Although the index results in area difference of 2.32 m2 compared to visual analysis, the combination of NIR-2 and red bands can give an accuracy of 96.29 %.
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Zhang, Hongxin, Xiaoyue Wang, and Dailiang Peng. "Evaluation of Urban Vegetation Phenology Using 250 m MODIS Vegetation Indices." Photogrammetric Engineering & Remote Sensing 88, no. 7 (2022): 461–67. http://dx.doi.org/10.14358/pers.21-00049r3.

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The dynamics of urban vegetation phenology play an important role in influencing human activities. Previous studies have shown high-resolution remote sensing as a tool for urban vegetation mapping, but the low temporal resolution of these data limits their use for phenological modeling. Therefore, it is of great significance to evaluate Moderate Resolution Imaging Spectroradiometer (MODIS) imagery for urban vegetation phenology monitoring. Here, we extracted the start and end of growing season (SOS and EOS) in urban ecosystems based on both the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) from the 250 m MODIS vegetarion indices product (MOD13Q1). Then the accuracies of the satellite-derived SOS and EOS were evaluated through comparing phenological observations at 18 ground sites. Results showed that SOS was most consistent with the prime of leaf unfolding date and EOS was most consistent with the beginning of leaf coloring date. Overall, EVI was found to have stronger predictive strength than NDVI in detecting urban vegetation phenology in terms of both higher correlation coef- ficients and lower root-mean-square errors. In addition, the dynamic threshold method was more accurate in deriving SOS, while the double logistic method had relatively higher accuracy in deriving EOS .
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You, Haotian, Qixu You, Xu Tang, Yao Liu, Jianjun Chen, and Feng Wang. "A Study on Spatial Distribution Extraction of Tidal Inundated Mangroves Based on High and Low Tide Level Images." Forests 14, no. 6 (2023): 1145. http://dx.doi.org/10.3390/f14061145.

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A majority of mangroves are located in the coastal intertidal zone and are subject to tidal periodic inundation. However, the previous vegetation indices used for extracting the spatial distribution of mangroves were not able to effectively extract submerged mangroves, and the applicability of the vegetation indices used on different spatial resolution images obtained from different sensors was not verified. In this study, a new vegetation index, namely the intertidal mangrove identification indices (IMIIs), was proposed, based on GF-2 images of high and low tide levels. Meanwhile, other commonly used vegetation indices were also extracted. All the vegetation indices were used to extract the spatial distribution of mangroves under tidal inundation, and applicability tests of the vegetation indices were conducted on Sentinel-2 images in three different regions. It was found that the IMIIs proposed based on GF-2 images of high and low tide levels can extract submerged mangroves relatively well, and the spatial distribution extraction results of mangroves are better than those of other vegetation indices, with IMII2 outperforming IMII1. At the same time, IMIIs have good applicability in medium resolution Sentinel-2 images, and there are relatively large differences in the extraction results of mangrove spatial distribution among different vegetation indices in areas with significant impact of tidal inundation. Among all vegetation indices, the extraction results of IMIIs are relatively superior. In most cases, multi variables collaborative application can improve the accuracy of mangrove spatial distribution extraction results. Based on the results of this study, it was concluded that the IMIIs proposed in this study can accurately extract the spatial distribution of mangroves inundated by tides from both medium- and high-resolution images, providing accurate basic data for effective management and scientific protection of mangrove resources.
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HENRIQUES, HERMANO JOSÉ RIBEIRO, DÁRIO ALEXANDRE SCHWAMBACH, VANESSA JORDÃO MARCATO FERNANDES, and JORGE WILSON CORTEZ. "VEGETATION INDICES AND THEIR CORRELATION WITH SECOND-CROP CORN GRAIN YIELD IN MATO GROSSO DO SUL, BRAZIL." Revista Brasileira de Milho e Sorgo 20 (March 24, 2021): 13. http://dx.doi.org/10.18512/rbms2021v20e1195.

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The emergence of satellites covering new electromagnetic wavelengthsallowed developing different vegetation indices, enabling the study of theircorrelation with grain yield. In this sense, this study aimed to evaluate the accuracy between the mean values of seven vegetation indices and the mean corn grain yield in the field by applying linear regression equations. The indices NDVI, NDRE, GNDVI, GRNDVI, and PNDVI were used, with changes proposed in the equations of the indices GRNDVI and PNDVI, in which the red wavelength was replaced by the red edge. The multispectral bands provided by the Sentinel-2A and Sentinel-2B imaging instruments were used as a source of data to calculate the vegetation indices, while the values recorded by the grain harvester were used for the survey of grain yield data. A high correlation was observed between indices and grain yield. The replacement of the red wavelength with the red edge improves the correlation between vegetation indices and grain yield. Moreover, the indices GNDVI and NDVI easily saturate, reaching maximum values and not allowing the distinction between sample classes. Therefore, the vegetation indices PRENDVI and GRENDVI are recommended for estimating grain yield.
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34

Piekarski, Paweł, and Zbigniew Zwoliński. "Temporal Variation in Vegetation Indexes for Pine and Beech Stands During the Vegetation Season, Szczecin Lowland, Poland." Quaestiones Geographicae 33, no. 3 (2014): 131–43. http://dx.doi.org/10.2478/quageo-2014-0037.

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Abstract Located in north-western Poland, the Bukowska Forest and Goleniowska Forest are vast woodlands consisting of areas with a homogeneous species composition that have been scarcely affected by humans. In this respect, they provided an excellent subject for scientific research, the purpose of which was to determine quantitative differences in selected vegetation indices of pine and beech stands in various periods during their vegetation seasons. Another purpose was to characterize the variation in these indices for each stand in its vegetation season. Four Landsat 5 TM images taken in 2007 and 2010 at four different points of vegetation season provided the basis for the analysis. In the analysis, 19 wooded areas with a homogeneous species composition were tested. In Bukowska Forest, the tested area was a beech stand, and in Goleniowska Forest, it was a pine stand. Acquired data was used to calculate the following vegetation indices: Normalized Difference Vegetation Index (NDVI), Transformed Vegetation Index (TVI), Green Normalized Difference Vegetation Index (Green NDVI), Normalized Difference Greenness Index (NDGI) and Normalized Difference Index (NDI). Subsequent research allowed to establish that the beech and pine stands differed significantly with respect to their calculated vegetation indices. These differences derived both from the biochemical and structural attributes of leaves and needles, as well as from transformations that occur in the stands during vegetation seasons. Analysis of the indices’ allowed us to determine these differences and the influence of the stands’ phenological phases on the indices.
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Xu, Xue, Luyao Liu, Peng Han, Xiaoqian Gong, and Qing Zhang. "Accuracy of Vegetation Indices in Assessing Different Grades of Grassland Desertification from UAV." International Journal of Environmental Research and Public Health 19, no. 24 (2022): 16793. http://dx.doi.org/10.3390/ijerph192416793.

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Grassland desertification has become one of the most serious environmental problems in the world. Grasslands are the focus of desertification research because of their ecological vulnerability. Their application on different grassland desertification grades remains limited. Therefore, in this study, 19 vegetation indices were calculated for 30 unmanned aerial vehicle (UAV) visible light images at five grades of grassland desertification in the Mu Us Sandy. Fractional Vegetation Coverage (FVC) with high accuracy was obtained through Support Vector Machine (SVM) classification, and the results were used as the reference values. Based on the FVC, the grassland desertification grades were divided into five grades: severe (FVC < 5%), high (FVC: 5–20%), moderate (FVC: 21–50%), slight (FVC: 51–70%), and non-desertification (FVC: 71–100%). The accuracy of the vegetation indices was assessed by the overall accuracy (OA), the kappa coefficient (k), and the relative error (RE). Our result showed that the accuracy of SVM-supervised classification was high in assessing each grassland desertification grade. Excess Green Red Blue Difference Index (EGRBDI), Visible Band Modified Soil Adjusted Vegetation Index (V-MSAVI), Green Leaf Index (GLI), Color Index of Vegetation Vegetative (CIVE), Red Green Blue Vegetation Index (RGBVI), and Excess Green (EXG) accurately assessed grassland desertification at severe, high, moderate, and slight grades. In addition, the Red Green Ratio Index (RGRI) and Combined 2 (COM2) were accurate in assessing severe desertification. The assessment of the 19 indices of the non-desertification grade had low accuracy. Moreover, our result showed that the accuracy of SVM-supervised classification was high in assessing each grassland desertification grade. This study emphasizes that the applicability of the vegetation indices varies with the degree of grassland desertification and hopes to provide scientific guidance for a more accurate grassland desertification assessment.
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36

GHOSH, K., R. P. SAMUI, and P. S. NARAYANAN. "Reflectance characteristics of maize and application of vegetation indices for estimation of leaf area index." MAUSAM 54, no. 4 (2022): 901–8. http://dx.doi.org/10.54302/mausam.v54i4.1590.

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An experiment was conducted on maize (Zea mays L), grown as a fodder crop, under irrigated and partially irrigated conditions with Ground Truth Radiometer at the experimental farm of the College of Agriculture, Pune under Mahatma Phule Krishi Vidyapith, Rahuri during pre-kharif season of 1999. Observations were taken at weekly intervals between 1000 and 1100 hrs. (IST) on cloud free days during the crop growing season. The spectral radiance characteristics and vegetation indices like Normalized Difference Vegetation Index (NDVI), Ratio Vegetation Index (RVI), Weighted Difference Vegetation Index (WDVI), Perpendicular Vegetation Index (PVI) and their relationship with Leaf Area Index (LAI) have been studied. The paper discusses the significance of spectral variation of radiance and sensitivity of vegetation indices with respect to phenological stages of the crop. The spectral reflectance observations in red and near infrared bands were recorded from the crop field. Vegetation indices were found suitable for crop growth studies. The procedure for estimating LAI using different vegetation indices is also discussed in the paper. It has been found that RVI is a better predictor of LAI during the early stage of growth while NDVI is a better predictor during the later part of growth as compared to other vegetation indices.
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Ha, Thuan, Yanben Shen, Hema Duddu, Eric Johnson, and Steven J. Shirtliffe. "Quantifying Hail Damage in Crops Using Sentinel-2 Imagery." Remote Sensing 14, no. 4 (2022): 951. http://dx.doi.org/10.3390/rs14040951.

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Hailstorms are a frequent natural weather disaster in the Canadian Prairies that can cause catastrophic damage to field crops. Assessment of damage for insurance claims requires insurance inspectors to visit individual fields and estimate damage on individual plants. This study computes temporal profiles and estimates the severity of hail damage to crops in 54 fields through the temporal analysis of vegetation indices calculated from Sentinel-2 images. The damage estimation accuracy of eight vegetative indices in different temporal analyses of delta index (pre-and post-hail differences) or area under curve (AUC) index (time profiles of index affected by hail) was compared. Hail damage was accurately quantified by using the AUC of 32 days of Normalized Difference Vegetation Indices (NDVI), Normalized Difference Water Index (NDWI), and Plant Senescence Radiation Index (PSRI). These metrics were well correlated with ground estimates of hail damage in canola (r = −0.90, RMSE = 8.24), wheat (r = −0.86, RMSE = 12.27), and lentil (r = 0.80, RMSE = 17.41). Thus, the time-series changes in vegetation indices had a good correlation with ground estimates of hail damage which may allow for more accurate assessment of the extent and severity of hail damage to crop land.
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38

Chen, Hao, Zhibao Dong, Shaopeng Song, Chao Li, and Xujia Cui. "Characteristics of the Soil and Vegetation along the Yulin–Jingbian Desert Expressway in China." Sustainability 11, no. 3 (2019): 606. http://dx.doi.org/10.3390/su11030606.

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Transportation infrastructure dramatically affects ecological processes. However, the environmental assessment process does not often consider how transportation impacts biodiversity, especially in ecologically fragile areas. The aim of this study was to assess the impacts of the Yulin–Jingbian expressway on vegetative diversity and to discuss the reason for the differences in soil-moisture distribution and vegetation diversity along the expressway. Samples were collected from 60 quadrats, along 6 transects. The α diversity indices and soil-moisture content calculated for each layer were used to represent habitat heterogeneity within a quadrat. A total of 49 species representing 39 genera and 16 families were recorded. Perennial herbs (42.9%) and annual herbs (36.7%) were the dominant life form. Species richness, diversity, and evenness indices of the vegetation varied with the distance between sampling points along the expressway. The vegetation with high diversity and evenness were near the expressway and areas with low diversity were farther from the expressway. The soil-moisture content in the 0–20 cm soil layer was a driving factor for the α diversity indices, and soil-moisture content below 20 cm played an inhibitory role on the α diversity indices. The greatest impact of the expressway on vegetation diversity was its effect on surface runoff and the distribution of plant root systems in the top layer of soil.
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Kim, Yunhee, Myeong-Hun Jeong, Minkyo Youm, Junkyeong Kim, and Jinpyung Kim. "Recovery of Forest Vegetation in a Burnt Area in the Republic of Korea: A Perspective Based on Sentinel-2 Data." Applied Sciences 11, no. 6 (2021): 2570. http://dx.doi.org/10.3390/app11062570.

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Forest fires are severe disasters that cause significant damage in the Republic of Korea and the entire world, and an effort is being made to prevent forest fires internationally. The Republic of Korea budgets 3.38 million USD every year to prevent forest fires. However, an average of 430 wildfires occur nationwide annually. Thirty-eight percent of the forest fire budget is used for forest restoration. Restoring afforestation in the affected areas is a top priority. This study aimed to estimate the degree of vegetative regeneration using the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil-Adjustment Vegetation Index (SAVI), and Normalized Burn Ratio (NBR). Although many studies have used NBR with NDVI to extract plant regeneration regions, they suffer from atmospheric effects and soil brightness. Thus, this study utilizes NBR with NDVI, EVI, and SAVI to accurately select areas for targeted forest restoration. Furthermore, this study applies clustering analysis to extract the spatial boundary of vegetative regenerative regions. The proposed method suggests a pixel range of vegetation indices. These ranges can be used as an indicator, such as the NBR’s Fire Severity Level, which reflects the mountain’s local characteristics, meaning that it can be useful after forest fires. Using the three vegetation indices can extract more accurate vegetation areas than using NBR with NDVI and can help determine a forest restoration target area.
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40

Safa, Mehran, Mirmasoud Kheirkhah Zarkesh, Farid Ejlali, and Forough Farsad4. "The Spatial Autocorrelation between Precipitation and Vegetation Indices in the Bandar Abbas Basin." International Journal of Scientific Research and Management 9, no. 12 (2021): 199–214. http://dx.doi.org/10.18535/ijsrm/v9i12.fe1.

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This study aimed to investigate the spatial autocorrelation between precipitation and vegetation indices in the Bandar Abbas basin. For this purpose, the vegetation indices of DVI, EVI, IPVI, NDVI, NDWI, RVI, SAVI, TCI, VCI, and VHI were derived from Landsat satellite images over 20 years were studied. Precipitation data corresponding to rain gauge stations was extracted. The Pearson correlation coefficient and the GI * and I indices were used to investigate the relationship between precipitation and spatial autocorrelation. Moreover, the Pearson correlation coefficient was used to investigate the relationship between precipitation and vegetation indices, and the GI * and I indices was used to correlate spatial autocorrelation patterns. The results showed that SAVI, VHI, VCI, and NDWI were most correlated with precipitation among the Bandar Abbas basin's vegetation indices, with the SAVI index being more closely correlated than the others. However, precipitation had the least impact on the TCI index. The spatial autocorrelation of rainfall with the vegetation indices, except for the IPVI index, had a scattered pattern in the study area’s southern and eastern parts. Of the indices studied in terms of spatial pattern, the IPVI and NDWI indices formed a positive spatial correlation pattern with precipitation over a greater spatial range.
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41

Ustuner, M., F. B. Sanli, S. Abdikan, M. T. Esetlili, and Y. Kurucu. "Crop Type Classification Using Vegetation Indices of RapidEye Imagery." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7 (September 19, 2014): 195–98. http://dx.doi.org/10.5194/isprsarchives-xl-7-195-2014.

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Cutting-edge remote sensing technology has a significant role for managing the natural resources as well as the any other applications about the earth observation. Crop monitoring is the one of these applications since remote sensing provides us accurate, up-to-date and cost-effective information about the crop types at the different temporal and spatial resolution. In this study, the potential use of three different vegetation indices of RapidEye imagery on crop type classification as well as the effect of each indices on classification accuracy were investigated. The Normalized Difference Vegetation Index (NDVI), the Green Normalized Difference Vegetation Index (GNDVI), and the Normalized Difference Red Edge Index (NDRE) are the three vegetation indices used in this study since all of these incorporated the near-infrared (NIR) band. RapidEye imagery is highly demanded and preferred for agricultural and forestry applications since it has red-edge and NIR bands. The study area is located in Aegean region of Turkey. Radial Basis Function (RBF) kernel was used here for the Support Vector Machines (SVMs) classification. Original bands of RapidEye imagery were excluded and classification was performed with only three vegetation indices. The contribution of each indices on image classification accuracy was also tested with single band classification. Highest classification accuracy of 87, 46 % was obtained using three vegetation indices. This obtained classification accuracy is higher than the classification accuracy of any dual-combination of these vegetation indices. Results demonstrate that NDRE has the highest contribution on classification accuracy compared to the other vegetation indices and the RapidEye imagery can get satisfactory results of classification accuracy without original bands.
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42

Liu, X., and M. Kafatos. "Land‐cover mixing and spectral vegetation indices." International Journal of Remote Sensing 26, no. 15 (2005): 3321–27. http://dx.doi.org/10.1080/01431160500056907.

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43

Sodnomov, B. V., A. A. Ayurzhanaev, B. Z. Tsydypov, E. Zh Garmaev, and A. K. Tulokhonov. "Software for analysis of vegetation indices dynamics." IOP Conference Series: Earth and Environmental Science 211 (December 17, 2018): 012083. http://dx.doi.org/10.1088/1755-1315/211/1/012083.

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44

Jaishanker, R., T. Senthivel, and V. N. Sridhar. "Comparison of vegetation indices for practicable homology." Journal of the Indian Society of Remote Sensing 33, no. 3 (2005): 395–404. http://dx.doi.org/10.1007/bf02990010.

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45

Szilagyi, Jozsef. "Vegetation Indices to Aid Areal Evapotranspiration Estimations." Journal of Hydrologic Engineering 7, no. 5 (2002): 368–72. http://dx.doi.org/10.1061/(asce)1084-0699(2002)7:5(368).

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46

Huete, Alfredo R. "Vegetation Indices, Remote Sensing and Forest Monitoring." Geography Compass 6, no. 9 (2012): 513–32. http://dx.doi.org/10.1111/j.1749-8198.2012.00507.x.

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47

Rego, Francisco C., Irene S. P. Cadima, and Eva K. Strand. "A Log-Ratio Vegetation Index (LRVI) for Discrimination and Classification of Remote Sensing Data." Silva Lusitana 28, no. 1 (2020): 45–70. http://dx.doi.org/10.1051/silu/20202801045.

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Discrimination and classification are integral processes for interpreting remotely sensed data. Many spectral vegetation indices have been proposed for discriminating between vegetation, soil, and other ground cover categories. Classical remote sensing show that reflectance in the red (R) and near infrared (NIR) bands of the electromagnetic spectrum have been successful in differentiating between vegetation and other ground cover classes and they are commonly used for this purpose. Here we demonstrate how Fisher’s classical statistics can be applied to develop discriminant functions for commonly used vegetation indices simply using the R and NIR bands. We derive a new vegetation index, the Log-Ratio Vegetation Index (LRVI) and demonstrate its utility in discriminating between cork oak trees and surrounding background in woodlands in Portugal. The LRVI performed better than seven previously developed vegetation indices, likely because of its linear properties in the reflectance density spectral space. The robustness and simplicity of LRVI suggests that it deserves further exploration and should be included for comparison with other vegetation indices and functions in discrimination, classification, and modelling studies. We suggest that the demonstrated approach is widely applicable to development of indices composed of other bands than R and NIR for systems or processes that correlate better with reflectance in other regions of the electromagnetic spectrum.
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48

Gulácsi, András, and Ferenc Kovács. "Drought Monitoring With Spectral Indices Calculated From Modis Satellite Images In Hungary." Journal of Environmental Geography 8, no. 3-4 (2015): 11–20. http://dx.doi.org/10.1515/jengeo-2015-0008.

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Abstract In this study a new remote sensing drought index called Difference Drought Index (DDI) was introduced. DDI was calculated from the Terra satellite’s MODIS sensor surface reflectance data using visible red, near-infrared and short-wave-infrared spectral bands. To characterize the biophysical state of vegetation, vegetation and water indices were used from which drought indices can be derived. The following spectral indices were examined: Difference Vegetation Index (DVI), Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Difference Water Index (DWI), Normalized Difference Water Index (NDWI), Difference Drought Index (DDI) and Normalized Difference Drought Index (NDDI). Regression analysis with the Pálfai Drought Index (PaDi) and average annual yield of different crops has proven that the Difference Drought Index is applicable in quantifying drought intensity. However, after comparison with reference data NDWI performed better than the other indices examined in this study. It was also confirmed that the water indices are more sensitive to changes in drought conditions than the vegetation ones. In the future we are planning to monitor drought during growing season using high temporal resolution MODIS data products.
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49

Al-Quraishi, Ayad, Hawar Razvanchy, and Heman Gaznayee. "A Comparative Study for Performance of Five Landsat-based Vegetation Indices: Their Relations to Some Ecological and Terrain Variables." Journal of Geoinformatics & Environmental Research 1, no. 1 (2020): 20–37. http://dx.doi.org/10.38094/jgier119.

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Spectral vegetation indices and their relations to some ecological and terrain variables in the Iraqi Kurdistan Region (IKR) is the main objective of this study. A mosaic of two Landsat-7 ETM+ images was utilized to produce five spectral vegetation indices, and Terra ASTER Digital Elevation Model (DEM) dataset were employed. The Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), Optimized Soil Adjusted Vegetation Index (OSAVI), Tasseled Cap Greenness, Land Surface Temperature (LST) were utilized for this study. The results of the current study revealed that MSAVI2 is more reliable and accurate in depicting the vegetation presence in the IKR, which is occupied 34.7% of the total study area in 2014. In terms of terrain variables, all vegetation indices responded to variation of aspect ratio variation. It was found that the densest vegetation exists between 180 to 350°. Mainly, in the South (157.5°-202.5°), Southwest (202.5°-247.5°), West (247.5°-292.5°), Northwest (292.5°-337.5°), and North (337.5°-360°). In contrast, from the aspect ratio point of view, vegetation cover growth was in its maximum status in the shaded side of the mountains, more than the sunny side. Additionally, the adequate slope for vegetation growth in the mountainous lands is 9-17%. Statistically, the LST appeared negative relations with vegetation indices and elevation
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Li, Yunqing, Jiancheng Shi, and Tianjie Zhao. "Effective vegetation optical depth retrieval using microwave vegetation indices from WindSat data for short vegetation." Journal of Applied Remote Sensing 9, no. 1 (2015): 096003. http://dx.doi.org/10.1117/1.jrs.9.096003.

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