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Relevant bibliographies by topics / Vegetation indices

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Journal articles

Academic literature on the topic 'Vegetation indices'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 June 2025

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Journal articles on the topic "Vegetation indices"

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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