Performance Evaluation of Long NDVI Timeseries from AVHRR, MODIS and Landsat Sensors over Landslide-Prone Locations in Qinghai-Tibetan Plateau
Abstract
:1. Introduction
- i.
- Evaluation of the performances of the five NDVI datasets in different land covers as well as over the predominant land cover types in QTP;
- ii.
- Identification of the most suitable sensor for NDVI time series analysis across QTP.
No | Topics of NDVI Application | NDVI-Product (Sensor)/Period of Study | Key Findings | Reference |
---|---|---|---|---|
1 | NDVI (Grassland phenology) and climate change relationship | GIMMS NOAA-AVHRR (15-day composite) (1982–2006) | NDVI significantly related to increasing temperatures, precipitation effects less pronounced. | [44] |
2 | NPP of Alpine Grassland; effect of topography and human activity | NOAA-AVHRR (10-day composite) MODIS (10-day composite) (1981–2004) | NPP (Net Primary Productivity) showed greater decreasing trend in high-elevation regions with slope 15–30°, aspect having little influence and roads having higher effect than residential areas. | [54] |
3 | Grassland vegetation cover and climatic factors | GIMMS NOAA-AVHRR (15-day composite) (1982–1999) | NDVI increase in growing season from both advanced growing season and accelerated vegetation activity. Apart from temperate steppe, NDVI exhibited no significant increase in autumn, corresponded to an increase in temperature in spring, and in summer, it was related to temperature and sensitive to precipitation in the spring. There were significant lagged correlations between precipitation and NDVI for alpine grasslands (alpine meadow, alpine steppe). | [55] |
4 | Vegetation greening and elevation | SPOT-VGT (10-day maximum value composite) (1999–2013) | NDVI increased more at lower elevations, but was relatively stable or even decreased at high elevations. Vegetation greening rate is in contrast to the pattern of elevation-dependent warming (EDW) with more significant temperature increases at higher elevations. Decreasing precipitation does not reverse overall increasing trend in NDVI, but it may be a limiting factor. | [56] |
5 | Vegetation response to temperature changes | GIMMS NOAA-AVHRR (15-day composite) (1981–1999) | Persistent increase in growing season NDVI and longer active growing season brought about by an early spring and delayed autumn. NDVI decreases possibly due to temperature-induced drought. Meaningful relation between changes in NDVI and land surface temperature. | [30] |
6 | Human impact on vegetation dynamics | SPOT-VGT (10-day maximum value composite) (1999–2013) | Impact of human activities in a relatively large area is minor and does not reverse the major trends of vegetation dynamics caused by the warming temperature in recent decades. | [25] |
7 | Spatial pattern of soil respiration in Tibetan alpine grasslands | Landsat TM MODIS (8-day composite) (2006) | NDVI exhibits spatial variation in soil respiration better than EVI and MSAVI. At the peak growing season of alpine grasslands, soil respiration was generally much higher in the SE Tibetan Plateau and gradually decreased toward NW part. | [57] |
8 | Monitoring vegetation phenology in Tibetan alpine grasslands | MODIS (16-day composite) C5 and C6 (2001–2015) | To evaluate the uncertainty of MODIS C5 and C6 NDVI in monitoring vegetation phenology, higher resolution near-surface remote sensing data and corresponding validation methods needed. | [29] |
9 | Vegetation classification | MODIS (16-day composite) (2004) | NDVI time-series curves of coniferous forest, high-cold meadow, high-cold meadow steppe and high-cold steppe all appear a mono-peak model during vegetation growth with the maximum peak occurring in August. | [58] |
10 | Sensitivity of NDVI to climate conditions | NOAA AVHRR GVI (1985–1999) | NDVI time series shows increasing trend of vegetation biomass. NDVI is strongly correlated but more sensitive to precipitation than temperature in semiarid climate zone of Lhasa area. | [59] |
11 | Variation in NPP | GIMMS NOAA-AVHRR (15-day composite) (1982–1999) | NPP decreases from the SE to the NW TP, consistent with precipitation and temperature patterns. NPP trends for most vegetation types resembled that of the whole plateau with alpine meadows showing the largest annual NPP increase. Changes in solar radiation and temperature significantly influenced NPP variation. | [60] |
12 | Vegetation and climate change | GIMMS NOAA-AVHRR (15-day composite) (1982–2012) | NDVI and temperature positively correlated during the growing season, responses to changes in precipitation were complex, particularly for different vegetation types. Growing season NDVI increased in 55% area of TP. | [27] |
13 | Vegetation trend | Landsat 5, 7, and 8 MODIS (16-day composite) (1990–2018) | Vegetation trend (NDVI product) for the entire Tibetan Plateau at 30 m spatial resolution for 1990–2018. | [3] |
14 | Green-up dates or Start of vegetation growing season (SOS) | GIMMS NOAA-AVHRR (15-day composite) SPOT VGT (10-day composite) MODIS (16-day composite) (1982–2011) | GIMMS NDVI in 2001–2006 differed substantially from SPOT-VGT and MODIS NDVIs and may have severe data quality issues in western TP. Alpine vegetation SOS in experienced continuous advancing trend in 1982–2011, consistent with observed warming in springs and winters. | [61] |
15 | Start of vegetation growing season (SGS) | GIMMS NOAA-AVHRR (15-day composite) SPOT VGT (10-day composite) (1982–2012) | SGS values display advancing trend with significant variations in SGS dates related to vegetation cover. Critical to know seasonal change characteristics of the different vegetation types, particularly in areas with sparse grassland or evergreen forest. | [53] |
2. Materials and Methods
2.1. Study Area
2.2. Data Used
2.2.1. NOAA-AVHRR-NDVI-V5
2.2.2. MODIS-NDVI (MOD13Q1-MYD13Q1)
2.2.3. Landsat Series (7 and 8)
2.2.4. Land Cover Dataset
2.3. Methods
2.3.1. Harmonic Analysis of Time Series (HANTS) Algorithm
2.3.2. Gaussian-Based Wavelet Threshold Denoising (WTD)
- i.
- Since abrupt changes caused by outliers influence the NDVI time series, it is essential to identify and eliminate these detail coefficients. The Gaussian-based thresholding method was used in this study. Hence, based on the experimental examination, the confidence interval of −σ ~ σ + μ (68.27%, is mean value and is standard deviation) was considered as an outlier, and those detail coefficients outside the confidence interval were removed (i.e., set as 0) [119,120].
- ii.
- Typical wavelet thresholding method “RIGRSURE” was performed to estimate threshold Tj [121] (in Equation (7) (Figure 3). The mathematical equation of the RIGRSURE threshold [122,123,124] can be defined as:
2.4. Overall and Localized Model Performance
3. Results
3.1. NDVI Time Series Denoising and Evaluation
3.2. Results of AVHRR Dataset
3.3. Results of Landsat Dataset (ETM+, OLI)
3.4. Results of MODIS Dataset (AQUA, TERRA)
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Sensors/Model | Temporal Resolution (day) | Spatial Resolution (m) | Bands | Data Source |
---|---|---|---|---|
NOAA-AVHRR-NDVI-V5 | 1 | 0.05° | NDVI | http://ltdr.nascom.nasa.gov (accessed on 10 July 2020 from GEE) |
MYD13Q1 | 16 | 250 | NDVI | https://lpdaac.usgs.gov/products/myd13q1v006/ (accessed on 10 July 2020 from GEE) |
MOD13Q1 | 16 | 250 | NDVI | https://lpdaac.usgs.gov/products/mod13q1v006/ (accessed on 10 July 2020 from GEE) |
Landsat 7 | 16 | 30 | NIR, R | http://landsat.usgs.gov/CDR_LSR (accessed on 10 July 2020 from GEE) |
Landsat 8 | 16 | 30 | NIR, R | http://landsat.usgs.gov/CDR_LSR (accessed on 10 July 2020 from GEE) |
Landcover Codes (LCC) | LC (%) | MOD13Q1 | MYD13Q1 | AVHRR | ETM+ | OLI | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
RMSE | CC | SNR | RMSE | CC | SNR | RMSE | CC | SNR | RMSE | CC | SNR | RMSE | CC | SNR | ||
Cropland, rainfed (10) | 8.90 | 0.043 | 0.941 | 20.505 | 0.048 | 0.939 | 20.299 | 0.025 | 0.965 | 25.423 | 0.026 | 0.967 | 24.081 | 0.021 | 0.976 | 27.092 |
Herbaceous cover (11) | 3.56 | 0.046 | 0.937 | 20.434 | 0.048 | 0.944 | 20.292 | 0.022 | 0.976 | 26.054 | 0.032 | 0.957 | 22.877 | 0.023 | 0.979 | 26.630 |
Cropland, irrigated (20) | 3.07 | 0.049 | 0.863 | 18.161 | 0.055 | 0.812 | 17.091 | 0.027 | 0.944 | 24.201 | 0.026 | 0.951 | 22.613 | 0.020 | 0.96 | 25.826 |
Mosaic cropland (30) | 7.28 | 0.048 | 0.919 | 20.634 | 0.047 | 0.913 | 20.210 | 0.022 | 0.966 | 26.448 | 0.025 | 0.958 | 23.717 | 0.021 | 0.971 | 27.839 |
Mosaic natural vegetation (40) | 15.37 | 0.050 | 0.905 | 20.156 | 0.058 | 0.877 | 19.556 | 0.022 | 0.973 | 27.332 | 0.029 | 0.961 | 23.635 | 0.021 | 0.980 | 28.354 |
Tree cover, broadleaved, evergreen (50) | 6.95 | 0.073 | 0.834 | 19.402 | 0.086 | 0.816 | 18.170 | 0.017 | 0.982 | 30.075 | 0.033 | 0.94 | 24.721 | 0.024 | 0.968 | 29.142 |
Deciduous closed to open (60) | 8.09 | 0.053 | 0.912 | 20.368 | 0.060 | 0.898 | 19.618 | 0.024 | 0.969 | 26.265 | 0.028 | 0.957 | 23.898 | 0.023 | 0.972 | 27.202 |
Broadleaved, deciduous, closed (61) | 3.56 | 0.055 | 0.903 | 20.448 | 0.065 | 0.877 | 19.061 | 0.020 | 0.973 | 28.246 | 0.030 | 0.953 | 24.626 | 0.020 | 0.974 | 29.102 |
Needle-leaved, evergreen, closed to open (70) | 22.97 | 0.067 | 0.869 | 19.263 | 0.066 | 0.865 | 18.491 | 0.021 | 0.974 | 27.452 | 0.033 | 0.961 | 23.653 | 0.028 | 0.966 | 26.393 |
Mosaic tree and shrub/herbaceous (100) | 1.45 | 0.065 | 0.888 | 19.100 | 0.063 | 0.86 | 19.107 | 0.020 | 0.972 | 28.112 | 0.029 | 0.965 | 25.330 | 0.028 | 0.971 | 25.821 |
Mosaic herbaceous/tree and shrub (110) | 5.17 | 0.058 | 0.88 | 18.143 | 0.063 | 0.849 | 17.409 | 0.023 | 0.966 | 26.937 | 0.031 | 0.963 | 23.864 | 0.022 | 0.962 | 27.762 |
Grassland (130) | 13.26 | 0.039 | 0.873 | 17.581 | 0.042 | 0.865 | 16.763 | 0.013 | 0.961 | 25.350 | 0.016 | 0.962 | 23.216 | 0.011 | 0.978 | 26.855 |
Overall Performance | 0.052 | 0.894 | 19.724 | 0.055 | 0.879 | 18.955 | 0.021 | 0.972 | 26.236 | 0.030 | 0.96 | 23.562 | 0.022 | 0.973 | 27.220 | |
Performance Range ** | 0.048 | 0.148 | 7.006 | 0.052 | 0.154 | 7.139 | 0.036 | 0.151 | 7.636 | 0.076 | 0.374 | 5.935 | 0.083 | 0.208 | 8.290 |
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Sajadi, P.; Sang, Y.-F.; 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. Remote Sens. 2021, 13, 3172. https://doi.org/10.3390/rs13163172
Sajadi P, Sang Y-F, 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. Remote Sensing. 2021; 13(16):3172. https://doi.org/10.3390/rs13163172
Chicago/Turabian StyleSajadi, Payam, Yan-Fang Sang, Mehdi Gholamnia, Stefania Bonafoni, Luca Brocca, Biswajeet Pradhan, and Amit Singh. 2021. "Performance Evaluation of Long NDVI Timeseries from AVHRR, MODIS and Landsat Sensors over Landslide-Prone Locations in Qinghai-Tibetan Plateau" Remote Sensing 13, no. 16: 3172. https://doi.org/10.3390/rs13163172