A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing

Huang, Sha; Tang, Lina; Hupy, Joseph P.; Wang, Yan; Shao, Guofan

doi:10.1007/s11676-020-01155-1

Cited by 693 publications

(410 citation statements)

References 32 publications

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“…In contrast, NDVI showed good prediction for S2 (R 2 0.75 and 0.80 in Trial 1 and 2, respectively), but not so good for L8 (R 2 0.24 and 0.26). Among the tested VIs, the study findings here are in accordance with several other studies that have reported that introducing the properties of the underlying soil in a VI minimizes the effects of the soil background on the vegetation signal [45,48,49,53]. Although NDVI is also a strong indicator of forest greenness and biomass [54][55][56][57] and it is often related to LAI [44], it has some problems in estimating the vegetation cover.…”

Section: Discussionsupporting

confidence: 89%

Section: Satellite Datamentioning

confidence: 99%

“…The Normalized Difference Vegetation Index (NDVI) is one of the most commonly applied VIs in forest studies and it is often related to LAI [44,45]. Despite its intensive use, however, NDVI has the drawback of being sensitive to the reflectivity of the soil in which the plant is rooted, which limits its potentiality as does the fact that it saturates in situations of dense canopy [45]. Therefore, to account for changes in soil optical properties and minimize the background effect, a group of Soil-Line VIs has been developed, such as the Soil-Adjusted Vegetation Index (SAVI), the Modified Soil-Adjusted Vegetation Index (MSAVI) and the Optimized Soil-Adjusted Vegetation Index (OSAVI), among others.…”

Section: Satellite Datamentioning

confidence: 99%

See 2 more Smart Citations

Assessing Canopy Responses to Thinnings for Sweet Chestnut Coppice with Time-Series Vegetation Indices Derived from Landsat-8 and Sentinel-2 Imagery

Prada¹,

Cabo

Hernández-Clemente

et al. 2020

Remote Sensing

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Forest management treatments often translate into changes in forest structure. Understanding and assessing how forests react to these changes is key for forest managers to develop and follow sustainable practices. A strategy to remotely monitor the development of the canopy after thinning using satellite imagery time-series data is presented. The aim was to identify optimal remote sensing Vegetation Indices (VIs) to use as time-sensitive indicators of the early response of vegetation after the thinning of sweet chestnut (Castanea Sativa Mill.) coppice. For this, the changes produced at the canopy level by different thinning treatments and their evolution over time (2014–2019) were extracted from VI values corresponding to two trials involving 33 circular plots (r = 10 m). Plots were subjected to one of the following forest management treatments: Control with no intervention (2800–3300 stems ha−1), Treatment 1, one thinning leaving a living stock density of 900–600 stems ha−1 and Treatment 2, a more intensive thinning, leaving 400 stems ha−1. Time series data from Landsat-8 and Sentinel-2 were collected to calculate values for different VIs. Canopy development was computed by comparing the area under curves (AUCs) of different VI time-series annually throughout the study period. Soil-Line VIs were compared to the Normalized Vegetation Index (NDVI) revealing that the Second Modified Chlorophyll Absorption Ratio Index (MCARI2) more clearly demonstrated canopy evolution tendencies over time than the NDVI. MCARI2 data from both L8 and S2 reflected how the influence of treatment on the canopy cover decreases over the years, providing significant differences in the thinning year and the year after. Metrics derived from the MCARI2 time-series also demonstrated the capacity of the canopy to recovery to pretreatment coverage levels. The AUC method generates a specific V-shaped time-signature, the vertex of which coincides with the thinning event and, as such, provides forest managers with another tool to assist decision making in the development of sustainable forest management strategies.

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Section: Discussionsupporting

confidence: 89%

Section: Satellite Datamentioning

confidence: 99%

Section: Satellite Datamentioning

confidence: 99%

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Assessing Canopy Responses to Thinnings for Sweet Chestnut Coppice with Time-Series Vegetation Indices Derived from Landsat-8 and Sentinel-2 Imagery

Prada¹,

Cabo

Hernández-Clemente

et al. 2020

Remote Sensing

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“…Considering the vegetation and bare soil with vegetation after the re-segmentation of the second layer, we applied the normalized differential vegetation index (NDVI) [28] to separate those ground objects according to the reflection feature differences of the vegetation in the near infrared band and the red band.…”

Section: ) Special Featuresmentioning

confidence: 99%

Identification and Evaluation of Urban Construction Waste with VHR Remote Sensing Using Multi-Feature Analysis and a Hierarchical Segmentation Method

et al. 2021

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With rapid urbanization, the disposal and management of urban construction waste have become the main concerns of urban management. The distribution of urban construction waste is characterized by its wide range, irregularity, and ease of confusion with the surrounding ground objects, such as bare soil, buildings, and vegetation. Therefore, it is difficult to extract and identify information related to urban construction waste by using the traditional single spectral feature analysis method due to the problem of spectral confusion between construction waste and the surrounding ground objects, especially in the context of very-high-resolution (VHR) remote sensing images. Considering the multi-feature analysis method for VHR remote sensing images, we propose an optimal method that combines morphological indexing and hierarchical segmentation to extract the information on urban construction waste in VHR images. By comparing the differences between construction waste and the surrounding ground objects in terms of the spectrum, geometry, texture, and other features, we selected an optimal feature subset to improve the separability of the construction waste and other objects; then, we established a classification model of knowledge rules to achieve the rapid and accurate extraction of construction waste information. We also chose two experimental areas of Beijing to validate our algorithm. By using construction waste separability quality evaluation indexes, the identification accuracy of construction waste in the two study areas was determined to be 96.6% and 96.2%, the separability indexes of the construction waste and buildings reached 1.000, and the separability indexes of the construction waste and vegetation reached 1.000 and 0.818. The experimental results show that our method can accurately identify the exposed construction waste and construction waste covered with a dust screen, and it can effectively solve the problem of spectral confusion between the construction waste and the bare soil, buildings, and vegetation.

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“…Gamarra et al (2016) Em seu estudo, Bagherzadeh et al (2020) confirmaram a utilidade do índice NDVI para expressar a variabilidade da vegetação em condições climáticas semi-áridas, além das relações entre características climáticas e crescimento da vegetação para programas de reabilitação em escala de bacias hidrográficas. Huang et al (2020) realizaram um estudo sobre a aplicação do NDVI na era do Sensoriamento Remoto, e concluíram que seu uso facilita as aplicações de sensoriamento remoto porque se correlaciona com o status de uma ampla variedade de propriedades da vegetação, simplificando assim o complexo. Os autores ainda declaram que o sensoriamento remoto mudou a maneira como os recursos terrestres são vistos, usados e gerenciados.…”

Section: íNdice De Vegetação Da Diferença Normalizada (Ndvi)unclassified

Aplicação do Índice de Vegetação por Diferença Normalizada (NDVI) na Caracterização da Cobertura Vegetativa de Juazeiro Do Norte – CE

Barros¹,

Farias²,

Marinho³

2020

Rev. Bras. Geog. Fis.

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Dados de sensoriamento remoto são fundamentais em pesquisas voltadas a estudos do comportamento da vegetação, assim como no monitoramento de fenômenos meteorológicos e ambientais. Nesse contexto, surgem mecanismos capazes de auxiliar estudos que constatem o que acontece no meio ambiente, em que o Índice de Vegetação por Diferença Normalizada (NDVI) é uma dessas ferramentas. O monitoramento preciso e oportuno das características da superfície da Terra fornece a base para uma melhor compreensão das interações e relações entre os fenômenos humanos e naturais visando um melhor uso e gerenciamento de recursos. Em função disso, o objetivo desse artigo é realizar a geração de um mapa temático da situação da cobertura vegetal do município de Juazeiro do Norte-CE a partir do (NDVI). Para a realização deste trabalho foi utilizado o Sistema de Informação Geográfica (SIG QGIS), versão 2.18. O NDVI foi calculado a partir de imagens de satélites obtidas de forma gratuita no site Earth Explorer. Destaca-se como resultados que o NDVI máximo foi 0,60901. Esse valor próximo a 1 (um) indica uma boa quantidade de cobertura vegetal densa. Dessa forma, a aplicação do NDVI foi relevante para identificar como se encontra a atual situação do município em relação à sua vegetação, pois esse índice pode auxiliar nas tomadas de decisões por parte da gestão pública no planejamento ambiental, uma vez que funciona como indicador de áreas verdes. Portanto, essa técnica pode melhorar a detecção de alterações na vegetação em estudos futuros.Application of the Normalized Difference Vegetation Index (NDVI) in the Characterization of the Vegetative Cover of Juazeiro Do Norte – CEA B S T R A C TRemote sensing data are fundamental in research focused on studies of vegetation behavior, as well as in the monitoring of meteorological and environmental phenomena. In this context, the Normalized Difference Vegetation Index (NDVI) has been widely used for monitoring and evaluating vegetation, as it is one of the first analytical products of remote sensing used to simplify the complexities of multispectral images. Thus, accurate and timely monitoring of earth's surface characteristics provides the basis for a better understanding of the interactions and relationships between human and natural phenomena aiming at better use and resource management. In this sense, the objective of this article is to generate a thematic map of the situation of the vegetation cover of the municipality of Juazeiro do Norte-CE from the (NDVI). The Geographic Information System (SIG QGIS), version 2.18, was used to carry out this work. NDVI was calculated from satellite images obtained free of charge on the Earth Explorer website. It is noteworthy as results that the maximum NDVI was 0.60901. This value close to 1 (one) indicates a good amount of dense vegetation cover. Thus, the application of NDVI was relevant to identify how the current situation of the municipality is found in relation to its vegetation, because this index can help in decision-making by public management in environmental planning, since it acts as an indicator of green areas. Therefore, this technique may improve the detection of changes in vegetation in future studies.Keywords: Vegetation Index, Remote sensing, Vegetable Cover.

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A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing

Cited by 693 publications

References 32 publications

Assessing Canopy Responses to Thinnings for Sweet Chestnut Coppice with Time-Series Vegetation Indices Derived from Landsat-8 and Sentinel-2 Imagery

Assessing Canopy Responses to Thinnings for Sweet Chestnut Coppice with Time-Series Vegetation Indices Derived from Landsat-8 and Sentinel-2 Imagery

Identification and Evaluation of Urban Construction Waste with VHR Remote Sensing Using Multi-Feature Analysis and a Hierarchical Segmentation Method

Aplicação do Índice de Vegetação por Diferença Normalizada (NDVI) na Caracterização da Cobertura Vegetativa de Juazeiro Do Norte – CE

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