Ndvi: Vegetation Change Detection Using Remote Sensing and Gis – A Case Study of Vellore District

Gandhi, G. Meera; Parthiban, S.; Thummalu, Nagaraj; Christy, A.

doi:10.1016/j.procs.2015.07.415

Cited by 492 publications

(288 citation statements)

References 6 publications

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“…Therefore, ±0.1 are selected as the thresholds for the classification. The selection of the thresholds is also supported by the finding that the NDVI threshold for bare soil was 0.1 [46]. The weight of each pixel within the moving window was calculated separately according to their categories.…”

Section: Weight Calculation Based On Temporal Ndvi Changementioning

confidence: 99%

A Spatio-Temporal Data Fusion Model for Generating NDVI Time Series in Heterogeneous Regions

et al. 2017

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Time series vegetation indices with high spatial resolution and high temporal frequency are important for crop growth monitoring and management. However, due to technical constraints and cloud contamination, it is difficult to obtain such datasets. In this study, a spatio-temporal vegetation index image fusion model (STVIFM) was developed to generate high spatial resolution Normalized Difference Vegetation Index (NDVI) time-series images with higher accuracy, since most of the existing methods have some limitations in accurately predicting NDVI in heterogeneous regions, or rely on very computationally intensive steps and land cover maps for heterogeneous regions. The STVIFM aims to predict the fine-resolution NDVI through understanding the contribution of each fine-resolution pixel to the total NDVI change, which was calculated from the coarse-resolution images acquired on two dates. On the one hand, it considers the difference in relationships between the fine-and coarse-resolution images on different dates and the difference in NDVI change rates at different growing stages. On the other hand, it neither needs to search similar pixels nor needs to use land cover maps. The Landsat-8 and MODIS data acquired over three test sites with different landscapes were used to test the spatial and temporal performance of the proposed model. Compared with the spatial and temporal adaptive reflectance fusion model (STARFM), enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the flexible spatiotemporal data fusion (FSDAF) method, the proposed STVIFM outperforms the STARFM and ESTARFM at three study sites and different stages when the land cover or NDVI changes were captured by the two pairs of fine-and coarse-resolution images, and it is more robust and less computationally intensive than the FSDAF.

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Section: Weight Calculation Based On Temporal Ndvi Changementioning

confidence: 99%

A Spatio-Temporal Data Fusion Model for Generating NDVI Time Series in Heterogeneous Regions

et al. 2017

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“…No. 1, April 2017 14 Gandhi et al, 2015;Alhawiti and Mitsova, 2016;Sonawane and Bhagat, 2017) that can be used to map soil erosion events. Soil erosion is a dynamic process requiring constant monitoring while keeping up-to-date information on its spatial distribution.…”

Section: South African Journal Ofmentioning

confidence: 99%

Mapping soil erosion in a quaternary catchment in Eastern Cape using geographic information system and remote sensing

Phinzi¹,

Ngetar²

2017

SA J of Geomatics

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“…NDVI is functionally equivalent to simple band ratios of TM images and can be described as NDVI = (NIR band -RED band)/ (NIR band + RED band), where: NIR band = spectral reflectance for band 4; RED band = spectral reflectance for band 3. Use of NDVI for this purpose has been well-justified by Gandhi et al (2015).…”

Section: Map Buildingmentioning

confidence: 99%

“…Remote sensing and GIS techniques have been widely used in the past by many researchers to identify the vegetation change (Billingsley, 1984;Xie et al, 2008;Gandhi et al, 2015). Remote sensing helps the researchers to observe the history of the vegetations through the satellite images taken via various bands of the spectrum which can be enhanced by algorithms such as NDVI (Gandhi et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…Remote sensing helps the researchers to observe the history of the vegetations through the satellite images taken via various bands of the spectrum which can be enhanced by algorithms such as NDVI (Gandhi et al, 2015). GIS acts as a valuable tool to overlay the different images produced for different periods in the past to identify the changes.…”

Section: Introductionmentioning

confidence: 99%

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Identification of Vegetation Change of Lower U Minh National Park of Vietnam from 1975 to 2015

Khanh¹,

Subasinghe²

2017

JTFE

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The vegetation of the Lower U Minh is an important ecosystem as it contributes to preserrve the nature while providing many bebefits. It is also used as an excellent indicator for identifying early signs of ecosystem change in the entire area. In order for that, however, an effective method should be used to reevaluate the change occurred during the past years. Use of remote sensing is the most effective method that serves for this purpose which was also used in the present study. In order to identify the vegetation change over a 40 years of time since 1975, three Landsat (TM) satellite images (1975, 1995 and 2015) were taken to develop the base maps which were then compared to identify the vegetation change of the national park. Using the base maps, six different vegetation types were identified using unsupervised and supervised classifications to build vegetation classification map with an overall accuracy of 86.33% and a kappa coefficient of 0.81. The results showed that multi-temporal Landsat images with the average resolution bear the ability to assess the vegetation coverage changes. Though the total extent of the National Park has not been changed during the study period, the extents of its vegetation types changed in different manners. The grass cover changed from 155.57 ha in 1975 to 643.24 in 2015 while extent of water changed from 315.24 ha to 194.92 ha during the same period. The extents of the grass cover and water were 884.95 and 697.60 ha respectively in 1995. The Melaleuca forest cover of different ages was also changed in significant manner during the study period.

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Ndvi: Vegetation Change Detection Using Remote Sensing and Gis – A Case Study of Vellore District

Cited by 492 publications

References 6 publications

A Spatio-Temporal Data Fusion Model for Generating NDVI Time Series in Heterogeneous Regions

A Spatio-Temporal Data Fusion Model for Generating NDVI Time Series in Heterogeneous Regions

Mapping soil erosion in a quaternary catchment in Eastern Cape using geographic information system and remote sensing

Identification of Vegetation Change of Lower U Minh National Park of Vietnam from 1975 to 2015

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