scholarly journals Using MODIS-NDVI Time Series to Quantify the Vegetation Responses to River Hydro-Geomorphology in the Wandering River Floodplain in an Arid Region

Water ◽  
2021 ◽  
Vol 13 (16) ◽  
pp. 2269
Author(s):  
Xarapat Ablat ◽  
Gaohuan Liu ◽  
Qingsheng Liu ◽  
Chong Huang
Keyword(s):  
Time Series ◽  
Arid Region ◽  
Water Resource Management ◽  
Floodplain Vegetation ◽  
Vegetation Growth ◽  
Modis Ndvi ◽  
Inundation Area ◽  
Average Maximum ◽  
Ndvi Time Series ◽  
Water Inundation

Vegetation, hydrology and geomorphology are three major elements of the floodplain ecosystem on Earth. Although the normalized difference vegetation index (NDVI) has been used extensively to characterize floodplain vegetation growth, vigour and biomass, methods for quantifying the various distinct responses of floodplain vegetation to hydro-geomorphological changes in different lateral belts in arid regions are still needed. In this study, the Linhe reach was divided into four lateral belts based on their hydro-geomorphological characteristics, and the Moderate Resolution Imaging Spectroradiometer (MODIS)-NDVI time series statistical indicators were used to characterise the distinct changing the patterns of vegetation growth in different belts. The response of floodplain vegetation to river hydro-geomorphology in each belt was analysed. The result showed that the average maximum NDVI value in the regular inundation area was 0.23 and higher than that in the other lateral belts. The correlation between the water persistence time and peak NDVI value in the regular water inundation area was significant (ρ = 0.84), indicating that in contrast to highly frequent or extremely rare water inundation, regular water inundation provides significant benefits to floodplains. Continuous or highly frequent inundation may cause decreased vegetation productivity. Overall, our results suggest that the vegetation greenness response to the river hydro-geomorphology is different from the river to the edge of the floodplain. Thus, a better understanding of the interactions between the floodplain vegetation and river hydro-morphology and river water resource management in arid-region floodplains.

scholarly journals Variation in Vegetation and Its Driving Force in the Middle Reaches of the Yangtze River in China

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2036
Author(s):  
Yang Yi ◽  
Bin Wang ◽  
Mingchang Shi ◽  
Zekun Meng ◽  
Chen Zhang
Keyword(s):  
Yangtze River ◽  
Remote Sensing Data ◽  
Slope Aspect ◽  
The Yangtze River ◽  
Vegetation Growth ◽  
Modis Ndvi ◽  
Sunshine Hours ◽  
Ndvi Time Series ◽  
Temporal And Spatial Characteristics ◽  
Afforestation Policy

The temporal and spatial characteristics of vegetation in the middle reaches of the Yangtze River (MRYR) were analyzed from 1999 to 2015 by trend analysis, co-integration analysis, partial correlation analysis, and spatial analysis using MODIS-NDVI time series remote sensing data. The average NDVI of the MRYR increased from 0.72 to 0.80, and nearly two-thirds of the vegetation showed a significant trend of improvement. At the inter-annual scale, the relationship between NDVI and meteorological factors was not significant in most areas. At the inter-monthly scale, NDVI was almost significantly correlated with precipitation, relative humidity, and sunshine hours, and the effect of precipitation and sunshine hours on NDVI showed a pronounced lag. When the altitude was less than 2500 m, NDVI increased with elevation. NDVI increased gradually as the slope increased and decreased gradually as the slope aspect changed from north to south. NDVI decreased as the population density and per capita GDP increased and was significantly positively correlated with afforestation policy. These findings provide new insights into the effects of climate change and human activities on vegetation growth.

Multi-year monitoring land surface phenology in relation to climatic variables using MODIS-NDVI time-series in Mediterranean forest, Northeast Tunisia

2022 ◽  
Vol 114 ◽  
pp. 103804
Author(s):  
Issam Touhami ◽  
Hassane Moutahir ◽  
Dorsaf Assoul ◽  
Kaouther Bergaoui ◽  
Hamdi Aouinti ◽  
...  
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Climatic Variables ◽  
Mediterranean Forest ◽  
Land Surface Phenology ◽  
Modis Ndvi ◽  
Ndvi Time Series

Wheat planted area detection from the MODIS NDVI time series classification using the nearest neighbour method calculated by the Euclidean distance and cosine similarity measures

2019 ◽  
Vol 35 (13) ◽  
pp. 1400-1414 ◽  
Author(s):  
Miriam Rodrigues da Silva ◽  
Osmar Abílio de Carvalho ◽  
Renato Fontes Guimarães ◽  
Roberto Arnaldo Trancoso Gomes ◽  
Cristiano Rosa Silva
Keyword(s):  
Time Series ◽  
Euclidean Distance ◽  
Similarity Measures ◽  
Cosine Similarity ◽  
Nearest Neighbour ◽  
Time Series Classification ◽  
Modis Ndvi ◽  
Ndvi Time Series ◽  
Cosine Similarity Measures

scholarly journals Mapping deciduous forests by using time series of filtered MODIS NDVI and neural networks

CERNE ◽  
2010 ◽  
Vol 16 (2) ◽  
pp. 123-130 ◽  
Author(s):  
Thomaz Chaves de Andrade Oliveira ◽  
Luis Marcelo Tavares de Carvalho ◽  
Luciano Teixeira de Oliveira ◽  
Adriana Zanella Martinhago ◽  
Fausto Weimar Acerbi Júnior ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Vegetation Indices ◽  
Deciduous Forests ◽  
Modis Ndvi ◽  
Minas Gerais State ◽  
Ndvi Time Series ◽  
Multi Temporal ◽  
Comparison Of The Results ◽  
Filtering Techniques

Multi-temporal images are now of standard use in remote sensing of vegetation during monitoring and classification. Temporal vegetation signatures (i. e., vegetation indices as functions of time) generated, poses many challenges, primarily due to signal to noise-related issues. This study investigates which methods generate the most appropriate smoothed curves of vegetation signatures on MODIS NDVI time series. The filtering techniques compared were the HANTS algorithm which is based on Fourier analyses and Wavelet temporal algorithm which uses the wavelet analysis to generate the smoothed curves. The study was conducted in four different regions of the Minas Gerais State. The smoothed data were used as input data vectors for vegetation classification by means of artificial neural networks for comparison purpose. A comparison of the results was ultimately discussed in this work showing encouraging results and similarity between the two filtering techniques used.

SURE-based optimum-length S-G filter to reconstruct NDVI time series iteratively with outliers removal

2019 ◽  
Vol 18 (02) ◽  
pp. 2050001
Author(s):  
Liang Tang ◽  
Zhongming Zhao ◽  
Ping Tang ◽  
Haijun Yang
Keyword(s):  
Time Series ◽  
Polynomial Regression ◽  
Random Noise ◽  
Simulated Data ◽  
Optimum Length ◽  
Modis Ndvi ◽  
Local Polynomial ◽  
Ndvi Time Series ◽  
Iterative Filtering ◽  
Unbiased Risk

Savitzky–Golay (S-G) filter is a method of local polynomial regression, and iterative filtering with S-G filter can be used to smooth out random noise and outliers of cloud noise in NDVI time series. It involves a continuous approximation to the upper envelope of NDVI time series. In this paper, the optimum-length of S-G filter was estimated based on Steinc’s unbiased risk estimator theory when S-G filtering was conducted iteratively, and the reconstruction result was presented. Reconstruction experiments on the simulated data and MODIS NDVI time series of the year 2010–2014 showed that the optimum-length S-G filter can outperform the fixed bandwidth S-G filter.

Sign in / Sign up

Export Citation Format

Share Document