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

Remote Sensing

Biswajeet Pradhan¹  Mehdi Gholamnia²  Amit Singh³  Stefania Bonafoni⁴  Yan-Fang Sang⁵  Payam Sajadi⁵  Luca Brocca⁶ 

[1] Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering & IT, University of Technology Sydney, Sydney, NSW 2007, Australia;Department of Civil Engineering, Sanandaj Branch, Islamic Azad University, Sanandaj 6616935391, Iran;Department of Energy and Environment, TERI School of Advanced Studies, New Delhi 110070, India;Department of Engineering, University of Perugia, 06125 Perugia, Italy;Key Laboratory of Water Cycle & Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;Research Institute for Geo-Hydrological Protection, National Research Council, 06128 Perugia, Italy;

关键词: HANTS;    NDVI;    reconstruction;    wavelet threshold denoising;    Qinghai-Tibetan Plateau;   

DOI  :  10.3390/rs13163172

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown