An Adaptive Noise Reduction Method for NDVI Time Series Data Based on S–G Filtering and Wavelet Analysis

Off the coast of the Boso Peninsula, there is a triple junction of the Pacific Plate, the Philippine Sea Plate, and the North American Plate and the Boso Peninsula is one of the seismically active areas in Japan. There are also epicenter areas such as the 1703 Genroku Kanto Earthquake (M8.2), the 1923 Taisho Kanto Earthquake (M7.9), and the Boso Slow Slip which occurs every 6 years, which are geologically interesting places. To estimate the subsurface resistivity structure of the whole Boso area, Magnetotelluric (MT) survey with 41 sites (inter-sites distance of 7 km) has been conducted in 2014-2016, using U43 (12 sites, 1 Hz sampling ; Tierra Technica) and MTU-5, 5A, net (41 sites, 15, 150, and 2400 Hz sampling; Phoenix Geophysics). However, the Boso area is greatly affected by leak current from DC-driven trains, factories, and power lines, so the observed data are contaminated by artificial noises. When we tried to apply the conventional noise reduction method (e.g., remote reference (Gamble et al., 1979) and BIRRP (Chave and Thomson, 2004)) in frequency domain, the obtained MT sounding curve was not ideal. In particular, the phase between the periods of 20 and 400 sec was close to 0 degrees. It suggests that the method used is insufficient to reduce the near-field effect for the Boso data. Thus, we developed a new noise reduction method using MSSA (Multi-channel Singular Spectrum Analysis) as a pre-processing method in time domain.The procedure is as follows;(1) Decompose 6 component data (Hx, Hy, Ex, Ey, Hxr and Hyr: H and E means magnetic and electric field, respectively, x and y indicates NS and EW component, and r denotes the reference field observed at a quiet station) using MSSA into 6&#215;M principal components (PCs). &#160;Here, M shows the window length of MSSA.(2) Check contribution and periods of each PC and eliminate the PCs which are corresponding to the longer periods of variation. That is &#8220;detrend&#8221; of the original data.(3) Apply the second MSSA to the detrended time series data to separate signals and noises shorter than 400 sec.(4) Calculating correlation coefficients between H and Hr and between E and Hr for each PC and select the PCs with higher correlation to reconstruct time series data to make MT analysis.Then, we perform MT analysis by BIRRP to estimate apparent resistivity,As a result, the coherences of H-Hr, and E-Hr were improved and the MT sounding curve became smoother than those results by the conventional noise reduction methods. This indicated that the effectiveness of the proposed noise reduction. However, further investigation in different periods and sites will be required.

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Noise Reduction Method for Time Series Data of plant. Growth with Adjustment of Cut off Frequency of Low Pass Filter.

Shokubutsu Kojo Gakkaishi ◽

10.2525/jshita.8.237 ◽

1996 ◽

Vol 8 (4) ◽

pp. 237-243

Author(s):

Hiroshi SHIMIZU

Keyword(s):

Time Series ◽

Plant Growth ◽

Noise Reduction ◽

Reduction Method ◽

Time Series Data ◽

Series Data ◽

Pass Filter ◽

Low Pass Filter ◽

Low Pass

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URBAN VEGETATION MAPPING BASED ON THE HJ-1 NDVI RECONSTRCTION

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b8-867-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 867-871

Author(s):

Feng Li ◽

Liu Han ◽

Zhu Liujun ◽

Huang Yinyou ◽

Guo Song

Keyword(s):

Time Series ◽

Noise Reduction ◽

Urban Areas ◽

Time Series Data ◽

Vegetation Mapping ◽

Series Data ◽

Urban Vegetation ◽

Satellite Platform ◽

Comparison Results ◽

Ndvi Time Series

HJ-1A/B NDVI (HJ NDVI) time-series data possess relatively high spatio-temporal resolution which is significant for the research on urban areas. However, its application is hindered by noise resulting from the restrictions of imaging quality and limits of the satellite platform. The NDVI noise reduction is necessary. Some noise-reduction techniques including the asymmetric Gaussian filter (AG), the double logistic filter (DL), the Savitzky-Golay (S-G) filter and the harmonic analysis (Hants) of NDVI time-series have been used to carry out the NDVI time series reconstruction, and based on the comparison results of different filter, S-G filter is the optimal in the application on urban areas. Finally,urban vegetation mapping is carried out based on the new HJ NDVI.

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URBAN VEGETATION MAPPING BASED ON THE HJ-1 NDVI RECONSTRCTION

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b8-867-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 867-871

Author(s):

Feng Li ◽

Liu Han ◽

Zhu Liujun ◽

Huang Yinyou ◽

Guo Song

Keyword(s):

Time Series ◽

Noise Reduction ◽

Urban Areas ◽

Time Series Data ◽

Vegetation Mapping ◽

Series Data ◽

Urban Vegetation ◽

Satellite Platform ◽

Comparison Results ◽

Ndvi Time Series

HJ-1A/B NDVI (HJ NDVI) time-series data possess relatively high spatio-temporal resolution which is significant for the research on urban areas. However, its application is hindered by noise resulting from the restrictions of imaging quality and limits of the satellite platform. The NDVI noise reduction is necessary. Some noise-reduction techniques including the asymmetric Gaussian filter (AG), the double logistic filter (DL), the Savitzky-Golay (S-G) filter and the harmonic analysis (Hants) of NDVI time-series have been used to carry out the NDVI time series reconstruction, and based on the comparison results of different filter, S-G filter is the optimal in the application on urban areas. Finally,urban vegetation mapping is carried out based on the new HJ NDVI.

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Prediction of Soil Organic Carbon based on Landsat 8 Monthly NDVI Data for the Jianghan Plain in Hubei Province, China

Remote Sensing ◽

10.3390/rs11141683 ◽

2019 ◽

Vol 11 (14) ◽

pp. 1683 ◽

Cited By ~ 6

Author(s):

Yangchengsi Zhang ◽

Long Guo ◽

Yiyun Chen ◽

Tiezhu Shi ◽

Mei Luo ◽

...

Keyword(s):

Time Series ◽

Organic Carbon ◽

Time Series Data ◽

Soil Mapping ◽

Digital Soil Mapping ◽

Series Data ◽

Landsat 8 ◽

Auxiliary Variables ◽

Jianghan Plain ◽

Ndvi Time Series

High-precision maps of soil organic carbon (SOC) are beneficial for managing soil fertility and understanding the global carbon cycle. Digital soil mapping plays an important role in efficiently obtaining the spatial distribution of SOC, which contributes to precision agriculture. However, traditional soil-forming factors (i.e., terrain or climatic factors) have weak variability in low-relief areas, such as plains, and cannot reflect the spatial variation of soil attributes. Meanwhile, vegetation cover hinders the acquisition of the direct information of farmland soil. Thus, useful environmental variables should be utilized for SOC prediction and the digital mapping of such areas. SOC has an important effect on crop growth status, and remote sensing data can record the apparent spectral characteristics of crops. The normalized difference vegetation index (NDVI) is an important index reflecting crop growth and biomass. This study used NDVI time series data rather than traditional soil-forming factors to map SOC. Honghu City, located in the middle of the Jianghan Plain, was selected as the study region, and the NDVI time series data extracted from Landsat 8 were used as the auxiliary variables. SOC maps were estimated through stepwise linear regression (SLR), partial least squares regression (PLSR), support vector machine (SVM), and artificial neural network (ANN). Ordinary kriging (OK) was used as the reference model, while root mean square error of prediction (RMSEP) and coefficient of determination of prediction (R2P) were used to evaluate the model performance. Results showed that SOC had a significant positive correlation in July and August (0.17, 0.29) and a significant negative correlation in January, April, and December (−0.23, −0.27, and −0.23) with NDVI time series data. The best model for SOC prediction was generated by ANN, with the lowest RMSEP of 3.718 and highest R2P of 0.391, followed by SVM (RMSEP = 3.753, R2P = 0.361) and PLSR (RMSEP = 4.087, R2P = 0.283). The SLR model was the worst model, with the lowest R2P of 0.281 and highest RMSEP of 3.930. ANN and SVM were better than OK (RMSEP = 3.727, R2P = 0.372), whereas PLSR and SLR were worse than OK. Moreover, the prediction results using single-data NDVI or short time series NDVI showed low accuracy. The effect of the terrain factor on SOC prediction represented unsatisfactory results. All these results indicated that the NDVI time series data can be used for SOC mapping in plain areas and that the ANN model can maximally extract additional associated information between NDVI time series data and SOC. This study presented an effective method to overcome the selection of auxiliary variables for digital soil mapping in plain areas when the soil was covered with vegetation. This finding indicated that the time series characteristics of NDVI were conducive for predicting SOC in plains.

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