Performance evaluation of long ndvi timeseries from avhrr, modis and landsat sensors over landslide-prone locations in qinghai-tibetan plateau

Sajadi, P; Sang, YF; Gholamnia, M; Bonafoni, S; Brocca, L; Pradhan, B; Singh, A

Performance evaluation of long ndvi timeseries from avhrr, modis and landsat sensors over landslide-prone locations in qinghai-tibetan plateau

Sajadi, P Sang, YF Gholamnia, M Bonafoni, S Brocca, L Pradhan, B

Singh, A

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Publisher:: MDPI AG
Publication Type:: Journal Article
Citation:: Remote Sensing, 2021, 13, (16), pp. 1-27
Issue Date:: 2021-08-02

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Field	Value	Language
dc.contributor.author	Sajadi, P
dc.contributor.author	Sang, YF
dc.contributor.author	Gholamnia, M
dc.contributor.author	Bonafoni, S
dc.contributor.author	Brocca, L
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054
dc.contributor.author	Singh, A
dc.date.accessioned	2022-02-21T04:53:53Z
dc.date.available	2022-02-21T04:53:53Z
dc.date.issued	2021-08-02
dc.identifier.citation	Remote Sensing, 2021, 13, (16), pp. 1-27
dc.identifier.issn	2072-4292
dc.identifier.issn	2072-4292
dc.identifier.uri	http://hdl.handle.net/10453/154730
dc.description.abstract	The existence of several NDVI products in Qinghai-Tibetan Plateau (QTP) makes it challenging to identify the ideal sensor for vegetation monitoring as an important factor for landslide detection studies. A pixel-based analysis of the NDVI time series was carried out to compare the performances of five NDVI products, including ETM+, OLI, MODIS Series, and AVHRR sensors in QTP. Harmonic analysis of time series and wavelet threshold denoising were used for reconstruction and denoising of the five NDVI datasets. Each sensor performance was assessed based on the behavioral similarity between the original and denoised NDVI time series, considering the preservation of the original shape and time series values by computing correlation coefficient (CC), mean absolute error (MAE), root mean square error (RMSE), and signal to noise ratio (SNR). Results indicated that the OLI slightly outperformed the other sensors in all performance metrics, especially in mosaic natural vegetation, grassland, and cropland, providing 0.973, 0.015, 0.022, and 27.220 in CC, MAE, RMSE, and SNR, respectively. AVHRR showed similar results to OLI, with the best results in the predominant type of land covers (needle-leaved, evergreen, closed to open). The MODIS series performs lower across all vegetation classes than the other sensors, which might be related to the higher number of artifacts observed in the original data. In addition to the satellite sensor comparison, the proposed analysis demonstrated the effectiveness and reliability of the implemented methodology for reconstructing and denoising different NDVI time series, indicating its suitability for long-term trend analysis of different natural land cover classes, vegetation monitoring, and change detection.
dc.language	English
dc.publisher	MDPI AG
dc.relation.ispartof	Remote Sensing
dc.relation.isbasedon	10.3390/rs13163172
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0203 Classical Physics, 0406 Physical Geography and Environmental Geoscience, 0909 Geomatic Engineering
dc.title	Performance evaluation of long ndvi timeseries from avhrr, modis and landsat sensors over landslide-prone locations in qinghai-tibetan plateau
dc.type	Journal Article
utslib.citation.volume	13
utslib.for	0203 Classical Physics
utslib.for	0406 Physical Geography and Environmental Geoscience
utslib.for	0909 Geomatic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2022-02-21T04:53:50Z
pubs.issue	16
pubs.publication-status	Published
pubs.volume	13
utslib.citation.issue	16

Abstract:

The existence of several NDVI products in Qinghai-Tibetan Plateau (QTP) makes it challenging to identify the ideal sensor for vegetation monitoring as an important factor for landslide detection studies. A pixel-based analysis of the NDVI time series was carried out to compare the performances of five NDVI products, including ETM+, OLI, MODIS Series, and AVHRR sensors in QTP. Harmonic analysis of time series and wavelet threshold denoising were used for reconstruction and denoising of the five NDVI datasets. Each sensor performance was assessed based on the behavioral similarity between the original and denoised NDVI time series, considering the preservation of the original shape and time series values by computing correlation coefficient (CC), mean absolute error (MAE), root mean square error (RMSE), and signal to noise ratio (SNR). Results indicated that the OLI slightly outperformed the other sensors in all performance metrics, especially in mosaic natural vegetation, grassland, and cropland, providing 0.973, 0.015, 0.022, and 27.220 in CC, MAE, RMSE, and SNR, respectively. AVHRR showed similar results to OLI, with the best results in the predominant type of land covers (needle-leaved, evergreen, closed to open). The MODIS series performs lower across all vegetation classes than the other sensors, which might be related to the higher number of artifacts observed in the original data. In addition to the satellite sensor comparison, the proposed analysis demonstrated the effectiveness and reliability of the implemented methodology for reconstructing and denoising different NDVI time series, indicating its suitability for long-term trend analysis of different natural land cover classes, vegetation monitoring, and change detection.

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