Sajadi, Payam; Sang, Yan Fang; Gholamnia, Mehdi; Bonafoni, Stefania; Brocca, Luca; Pradhan, Biswajeet; Singh, Amit, 2021, Performance evaluation of long ndvi timeseries from avhrr, modis and landsat sensors over landslide-prone locations in qinghai-tibetan plateau, Remote sensing (Basel) 13 (2021). doi_10.3390/rs13163172,
URL: http://www.cnr.it/prodotto/i/457845

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.

Data from https://intranet.cnr.it/people/