An Improved Method for Producing High Spatial-Resolution NDVI Time Series Datasets with Multi-Temporal MODIS NDVI Data and Landsat TM/ETM+ Images

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.

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A SPIRAL-BASED DOWNSCALING METHOD FOR GENERATING 30 M TIME SERIES IMAGE DATA

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

10.5194/isprs-archives-xlii-2-w7-817-2017 ◽

2017 ◽

Vol XLII-2/W7 ◽

pp. 817-820

Author(s):

B. Liu ◽

J. Chen ◽

H. Xing ◽

H. Wu ◽

J. Zhang

Keyword(s):

Remote Sensing ◽

Time Series ◽

Land Cover ◽

Spatial Resolution ◽

High Spatial Resolution ◽

Image Data ◽

Resolution Time ◽

Simulated Experiment ◽

Land Cover Data ◽

Multi Temporal

The spatial detail and updating frequency of land cover data are important factors influencing land surface dynamic monitoring applications in high spatial resolution scale. However, the fragmentized patches and seasonal variable of some land cover types (e. g. small crop field, wetland) make it labor-intensive and difficult in the generation of land cover data. Utilizing the high spatial resolution multi-temporal image data is a possible solution. Unfortunately, the spatial and temporal resolution of available remote sensing data like Landsat or MODIS datasets can hardly satisfy the minimum mapping unit and frequency of current land cover mapping / updating at the same time. The generation of high resolution time series may be a compromise to cover the shortage in land cover updating process. One of popular way is to downscale multi-temporal MODIS data with other high spatial resolution auxiliary data like Landsat. But the usual manner of downscaling pixel based on a window may lead to the underdetermined problem in heterogeneous area, result in the uncertainty of some high spatial resolution pixels. Therefore, the downscaled multi-temporal data can hardly reach high spatial resolution as Landsat data. <br><br> A spiral based method was introduced to downscale low spatial and high temporal resolution image data to high spatial and high temporal resolution image data. By the way of searching the similar pixels around the adjacent region based on the spiral, the pixel set was made up in the adjacent region pixel by pixel. The underdetermined problem is prevented to a large extent from solving the linear system when adopting the pixel set constructed. With the help of ordinary least squares, the method inverted the endmember values of linear system. The high spatial resolution image was reconstructed on the basis of high spatial resolution class map and the endmember values band by band. Then, the high spatial resolution time series was formed with these high spatial resolution images image by image. <br><br> Simulated experiment and remote sensing image downscaling experiment were conducted. In simulated experiment, the 30 meters class map dataset Globeland30 was adopted to investigate the effect on avoid the underdetermined problem in downscaling procedure and a comparison between spiral and window was conducted. Further, the MODIS NDVI and Landsat image data was adopted to generate the 30m time series NDVI in remote sensing image downscaling experiment. Simulated experiment results showed that the proposed method had a robust performance in downscaling pixel in heterogeneous region and indicated that it was superior to the traditional window-based methods. The high resolution time series generated may be a benefit to the mapping and updating of land cover data.

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Assessing the potential of using high spatial resolution daily NDVI-time-series from planet CubeSat images for crop monitoring

International Journal of Remote Sensing ◽

10.1080/01431161.2021.1939908 ◽

2021 ◽

Vol 42 (18) ◽

pp. 7114-7142

Author(s):

Luís Guilherme Teixeira Crusiol ◽

Liang Sun ◽

Ruiqing Chen ◽

Zheng Sun ◽

Dejun Zhang ◽

...

Keyword(s):

Time Series ◽

Spatial Resolution ◽

High Spatial Resolution ◽

Crop Monitoring ◽

Ndvi Time Series

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Extraction of Agricultural Phenological Parameters of Sri Lanka Using MODIS, NDVI Time Series Data

Procedia Food Science ◽

10.1016/j.profoo.2016.02.027 ◽

2016 ◽

Vol 6 ◽

pp. 235-241 ◽

Cited By ~ 15

Author(s):

W.G.N.N. Jayawardhana ◽

V.M.I. Chathurange

Keyword(s):

Time Series ◽

Sri Lanka ◽

Time Series Data ◽

Series Data ◽

Modis Ndvi ◽

Ndvi Time Series

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Variação Espacial e Temporal de NDVI na Lagoa do Peixe, RS (Spatial and Temporal Variation of NDVI in the Peixe Lagoon, RS)

Revista Brasileira de Geografia Física ◽

10.26848/rbgf.v4i5.232706 ◽

2012 ◽

Vol 4 (5) ◽

pp. 897 ◽

Cited By ~ 1

Author(s):

Luana Portz ◽

Laurindo Antonio Guasselli ◽

Iran Carlos Stalliviere Corrêa

Keyword(s):

Time Series ◽

Temporal Variation ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Analysis Data ◽

Landsat Tm ◽

Spatial And Temporal Variation ◽

Rainfall Analysis ◽

Ndvi Time Series ◽

High Precipitation

Neste estudo foram analisadas as variações espaciais e temporais do Índice de Vegetação por Diferença Normalizada (NDVI) na lagoa do Peixe, no litoral do Rio Grande do Sul. Para alcançar o objetivo proposto foram utilizadas imagens de satélite Landsat TM5, entre os anos de 1986 e 2009, seguindo os procedimentos de elaboração de mosaico das cenas, verificação de campo, geração das imagens de NDVI, análise de dados de precipitação acumulada, geração dos mapas finais e análise qualitativa dos resultados obtidos. Os resultados obtidos com a geração de imagens de NDVI mostraram que a análise espaço-temporal associada aos dados de precipitação fornecem informações de valiosa importância sobre a dinâmica da lagoa do Peixe. A importância do NDVI neste estudo se destaca pelo contraste existente entre água e vegetação, realçando os diferentes níveis de água sobre os bancos vegetados presentes na borda oeste da lagoa. Estes bancos são um importante controlador da dinâmica de circulação lagunar, onde em períodos de seca ocorre a compartimentação da lagoa, enquanto que em épocas de grande precipitação e acumulação de água estes bancos ficam submersos. Palavras-chave: Landsat TM, série temporal, Parque Nacional. Spatial and Temporal Variation of NDVI in the Peixe Lagoon, RS ABSTRACTThis paper analyzed the spatial and temporal variation of Normalized Difference Vegetation Index (NDVI) in the Peixe lagoon. To reach the purpose, the NDVI time-series were collected from the study area between year 1986 and 2009 derived from Landsat TM5 satellite. The adopted methodology may be subdivided into the following steps: mosaic of scenes, fild verification, generation of NDVI time-series and qualitative analysis, in addition, it was complemented with rainfall analysis. The results obtained with the NDVI time-series associated with the rainfall analysis data provide valuable information about the environmental dynamics. The importance of NDVI in this work is given by the contrast between water and vegetation, highlighting the different levels of water over vegetated banks present on the western edge of the lagoon. These banks are an important driver circulation in the lagoon, where in periods of drought occurs the partitioning of the lagoo, while in periods of high precipitation and accumulation of water they are submerged. Keywords: Landsat TM, time-series, National Park.

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Classifying Wetland Vegetation Type from MODIS NDVI Time Series Using Fourier Analysis

Communications in Computer and Information Science - Applied Informatics and Communication ◽

10.1007/978-3-642-23214-5_9 ◽

2011 ◽

pp. 66-73 ◽

Cited By ~ 2

Author(s):

Xiaodong Na ◽

Shuying Zang

Keyword(s):

Time Series ◽

Fourier Analysis ◽

Vegetation Type ◽

Wetland Vegetation ◽

Modis Ndvi ◽

Ndvi Time Series

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Classification of multi-temporal spectral indices for crop type mapping: a case study in Coalville, UK

The Journal of Agricultural Science ◽

10.1017/s0021859617000879 ◽

2018 ◽

Vol 156 (1) ◽

pp. 24-36 ◽

Cited By ~ 15

Author(s):

Y. Palchowdhuri ◽

R. Valcarce-Diñeiro ◽

P. King ◽

M. Sanabria-Soto

Keyword(s):

Spatial Resolution ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Reference Data ◽

High Spatial Resolution ◽

Classification Algorithms ◽

Type Mapping ◽

Crop Type ◽

Multi Temporal ◽

Very High

AbstractRemote sensing (RS) offers an efficient and reliable means to map features on Earth. Crop type mapping using RS at various temporal and spatial resolutions plays an important role spanning from environmental to economical. The main objective of the current study was to evaluate the significance of optical data in a multi-temporal crop type classification-based on very high spatial resolution and high spatial resolution imagery. With this aim, three images from WorldView-3 and Sentinel-2 were acquired over Coalville (UK) between April and July 2016. Three vegetation indices (VIs); the normalized difference vegetation index, the green normalized difference vegetation index and soil adjusted vegetation index were generated using red, green and near-infrared spectral bands; then a supervised classification was performed using ground reference data collected from field surveys, Random forest (RF) and decision tree (DT) classification algorithms. Accuracy assessment was undertaken by comparing the classified output with the reference data. An overall accuracy of 91% and κ coefficient of 0·90 were estimated using the combination of RF and DT classification algorithms. Therefore, it can be concluded that integrating very high- and high-resolution imagery with different VIs can be implemented effectively to produce large-scale crop maps even with a limited temporal-dataset.

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