Performance Evaluation of Long NDVI Timeseries from AVHRR, MODIS and Landsat Sensors over Landslide-Prone Locations in Qinghai-Tibetan Plateau

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 fi...

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Published inRemote sensing (Basel, Switzerland) Vol. 13; no. 16; p. 3172
Main Authors Sajadi, Payam, Sang, Yan-Fang, Gholamnia, Mehdi, Bonafoni, Stefania, Brocca, Luca, Pradhan, Biswajeet, Singh, Amit
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 11.08.2021
Subjects
Agricultural land
China
Climate change
Correlation coefficient
Correlation coefficients
cropland
data collection
Datasets
Fourier analysis
Fourier transforms
Grasslands
HANTS
Harmonic analysis
Land cover
Landsat
Landsat satellites
Landslides
Landslides & mudslides
MODIS
Monitoring
Natural vegetation
NDVI
Noise
Noise reduction
Performance evaluation
Performance measurement
Phenology
Pine needles
Precipitation
Qinghai-Tibetan Plateau
reconstruction
Reliability analysis
Remote sensing
Respiration
Root-mean-square errors
Satellites
Seasons
Sensors
Signal to noise ratio
Time series
time series analysis
Topography
Trend analysis
Trends
Vegetation
wavelet
wavelet threshold denoising
Tibetan Plateau
China
Online AccessGet full text
ISSN2072-4292
2072-4292
DOI10.3390/rs13163172

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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.
AbstractList 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.
Author Gholamnia, Mehdi
Pradhan, Biswajeet
Singh, Amit
Bonafoni, Stefania
Brocca, Luca
Sajadi, Payam
Sang, Yan-Fang
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  fullname: Singh, Amit
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Snippet The existence of several NDVI products in Qinghai-Tibetan Plateau (QTP) makes it challenging to identify the ideal sensor for vegetation monitoring as an...
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StartPage 3172
SubjectTerms Agricultural land
China
Climate change
Correlation coefficient
Correlation coefficients
cropland
data collection
Datasets
Fourier analysis
Fourier transforms
Grasslands
HANTS
Harmonic analysis
Land cover
Landsat
Landsat satellites
Landslides
Landslides & mudslides
MODIS
Monitoring
Natural vegetation
NDVI
Noise
Noise reduction
Performance evaluation
Performance measurement
Phenology
Pine needles
Precipitation
Qinghai-Tibetan Plateau
reconstruction
Reliability analysis
Remote sensing
Respiration
Root-mean-square errors
Satellites
Seasons
Sensors
Signal to noise ratio
Time series
time series analysis
Topography
Trend analysis
Trends
Vegetation
wavelet
wavelet threshold denoising
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Title Performance Evaluation of Long NDVI Timeseries from AVHRR, MODIS and Landsat Sensors over Landslide-Prone Locations in Qinghai-Tibetan Plateau
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