scholarly journals FORECASTING TIME SERIES CHANGE OF THE AVERAGE ENHANCED VEGETATION INDEX TO MONITORING DROUGHT CONDITION BY USING TERRA/MODIS DATA

2021 ◽  
Vol 67 (4) ◽  
Author(s):  
Satith SANGPRADID ◽  
Yannawut UTTARUK ◽  
Tanutdech ROTJANAKUSOL ◽  
Teerawong LAOSUWAN
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Drought Condition ◽  
Enhanced Vegetation Index ◽  
Modis Data ◽  
Terra Modis

scholarly journals Phenological-metric algorithm for mapping soybean in savanna biome in Brazil

2019 ◽  
pp. 1456-1466
Author(s):  
Bernard S. de Oliveira ◽  
Manuel E. Ferreira ◽  
Alexandre C. Coutinho ◽  
Júlio C. D. M. Esquerdo
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Census Data ◽  
Pearson Correlation ◽  
Original Data ◽  
Landsat 8 ◽  
Interpolation Filter ◽  
Modis Evi ◽  
Terra Modis ◽  
Global Accuracy

Agricultural expansion in Brazil is still intense for commodities (such soybeans and corn), mostly cultivated over large portions of the Cerrado biome. Therefore, the development and application of techniques based on remote sensing to map crop areas at a regional level, in a dynamic and more precise way is urgently necessary. In this context, the objective of this study is the improvement of techniques for mapping soybean crops in Brazil, through an analysis of the Centro Goiano mesoregion of Goiás state (a core area of Cerrado), using a time series of Enhanced Vegetation Index (EVI) images provided by TERRA/MODIS orbital sensor, in a test period between 2002 and 2010. Despite their proven quality, MODIS EVI images already contain atmospheric interferences inherent to the acquisition process, such as the presence of clouds. Thus, a set of methods to minimize such artifacts was applied to the data of this study. In general, the methodological procedures comprise of (1) the application of the pixel reliability band aiming to remove pixels contaminated by clouds; (2) the use of contaminated pixel estimates (excluded from the time series); (3) application of an interpolation filter to fill the void pixels in each scene, obtaining continuous and smoothed spectral-temporal profiles for each land use classes; and (4) the classification of agricultural areas using a specific algorithm for crops in the Cerrado region of Goiás. The areas reconstituted in the images matched neighboring pixels, maintaining good coherence with the original data. Likewise, areas mapped with soybeans had a high correlation with official IBGE census data, with a global accuracy value of 78%, and Pearson Correlation coefficient of 0.64. The application of this technique to other imagery sensors (such as RapidEye, Landsat 8 and Sentinel 2) is highly encouraged due a better spatial and temporal resolution (when applied together in a temporal image cube), ensuring more efficient crop monitoring in Brazil.

scholarly journals Estimating crop area using seasonal time series of Enhanced Vegetation Index from MODIS satellite imagery

2007 ◽  
Vol 58 (4) ◽  
pp. 316 ◽  
Author(s):  
A. B. Potgieter ◽  
A. Apan ◽  
P. Dunn ◽  
G. Hammer
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
High Accuracy ◽  
Correct Classification ◽  
Pixel Classification ◽  
Study Region ◽  
Enhanced Vegetation Index ◽  
Multi Temporal ◽  
Crop Area ◽  
Pixel Scale

Cereal grain is one of the main export commodities of Australian agriculture. Over the past decade, crop yield forecasts for wheat and sorghum have shown appreciable utility for industry planning at shire, state, and national scales. There is now an increasing drive from industry for more accurate and cost-effective crop production forecasts. In order to generate production estimates, accurate crop area estimates are needed by the end of the cropping season. Multivariate methods for analysing remotely sensed Enhanced Vegetation Index (EVI) from 16-day Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery within the cropping period (i.e. April–November) were investigated to estimate crop area for wheat, barley, chickpea, and total winter cropped area for a case study region in NE Australia. Each pixel classification method was trained on ground truth data collected from the study region. Three approaches to pixel classification were examined: (i) cluster analysis of trajectories of EVI values from consecutive multi-date imagery during the crop growth period; (ii) harmonic analysis of the time series (HANTS) of the EVI values; and (iii) principal component analysis (PCA) of the time series of EVI values. Images classified using these three approaches were compared with each other, and with a classification based on the single MODIS image taken at peak EVI. Imagery for the 2003 and 2004 seasons was used to assess the ability of the methods to determine wheat, barley, chickpea, and total cropped area estimates. The accuracy at pixel scale was determined by the percent correct classification metric by contrasting all pixel scale samples with independent pixel observations. At a shire level, aggregated total crop area estimates were compared with surveyed estimates. All multi-temporal methods showed significant overall capability to estimate total winter crop area. There was high accuracy at pixel scale (>98% correct classification) for identifying overall winter cropping. However, discrimination among crops was less accurate. Although the use of single-date EVI data produced high accuracy for estimates of wheat area at shire scale, the result contradicted the poor pixel-scale accuracy associated with this approach, due to fortuitous compensating errors. Further studies are needed to extrapolate the multi-temporal approaches to other geographical areas and to improve the lead time for deriving cropped-area estimates before harvest.

scholarly journals Phenological response of vegetation to upstream river flow in the Heihe Rive basin by time series analysis of MODIS data

2011 ◽  
Vol 15 (3) ◽  
pp. 1047-1064 ◽  
Author(s):  
L. Jia ◽  
H. Shang ◽  
G. Hu ◽  
M. Menenti
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Time Series Data ◽  
Series Data ◽  
Water Yield ◽  
Heihe River ◽  
Transform Method ◽  
Ejina Oasis ◽  
Ndvi Time Series ◽  
Terra Modis

Abstract. Liquid and solid precipitation is abundant in the high elevation, upper reach of the Heihe River basin in northwestern China. The development of modern irrigation schemes in the middle reach of the basin is taking up an increasing share of fresh water resources, endangering the oasis and traditional irrigation systems in the lower reach. In this study, the response of vegetation in the Ejina Oasis in the lower reach of the Heihe River to the water yield of the upper catchment was analyzed by time series analysis of monthly observations of precipitation in the upper and lower catchment, river streamflow downstream of the modern irrigation schemes and satellite observations of vegetation index. Firstly, remotely sensed NDVI data acquired by Terra-MODIS are used to monitor the vegetation dynamic for a seven years period between 2000 and 2006. Due to cloud-contamination, atmospheric influence and different solar and viewing angles, however, the quality and consistence of time series of remotely sensed NDVI data are degraded. A Fourier Transform method – the Harmonic Analysis of Time Series (HANTS) algorithm – is used to reconstruct cloud- and noise-free NDVI time series data from the Terra-MODIS NDVI dataset. Modification is made on HANTS by adding additional parameters to deal with large data gaps in yearly time series in combination with a Temporal-Similarity-Statistics (TSS) method developed in this study to seek for initial values for the large gap periods. Secondly, the same Fourier Transform method is used to model time series of the vegetation phenology. The reconstructed cloud-free NDVI time series data are used to study the relationship between the water availability (i.e. the local precipitation and upstream water yield) and the evolution of vegetation conditions in Ejina Oasis from 2000 to 2006. Anomalies in precipitation, streamflow, and vegetation index are detected by comparing each year with the average year. The results showed that: the previous year total runoff had a significant relationship with the vegetation growth in Ejina Oasis and that anomalies in the spring monthly runoff of the Heihe River influenced the phenology of vegetation in the entire oasis. Warmer climate expressed by the degree-days showed positive influence on the vegetation phenology in particular during drier years. The time of maximum green-up is uniform throughout the oasis during wetter years, but showed a clear S-N gradient (downstream) during drier years.

scholarly journals Time Series Analysis of MODIS-Derived NDVI for the Hluhluwe-iMfolozi Park, South Africa: Impact of Recent Intense Drought

2018 ◽  
Author(s):  
Nkanyiso Mbatha ◽  
Sifiso Xulu
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Southern Oscillation ◽  
Kendall Test ◽  
Google Earth ◽  
Maximum Temperature ◽  
Sudden Increase ◽  
Area Index ◽  
Enhanced Vegetation Index ◽  
Temperature And Precipitation

The variability of meteorological parameters such as temperature and precipitation, and climatic conditions such as intense droughts, are known to impact vegetation health over southern Africa. Thus, understanding large-scale ocean–atmospheric phenomena like the El Niño/Southern Oscillation (ENSO) and Indian Ocean Dipole/Dipole Mode Index (DMI) is important as these factors drive the variability of temperature and precipitation. In this study, 16 years (2002–2017) of Moderate Resolution Imaging Spectroradiometer (MODIS) Terra/Aqua 16-day normalized difference vegetation index (NDVI), extracted and processed using JavaScript code editor in the Google Earth Engine (GEE) platform in order to analyze the response pattern of the oldest proclaimed nature reserve in Africa, the Hluhluwe-iMfolozi Park (HiP), during the study period. The MODIS-enhanced vegetation index and burned area index were also analyzed for this period. The area-averaged Modern Retrospective Analysis for Research Application (MERRA) model maximum temperature and precipitation were also extracted using the JavaScript code editor in the GEE platform. This procedure demonstrated a strong reversal of both the NDVI and Enhanced Vegetation Index (EVI), leading to signs of a sudden increase of burned areas (strong BAI) during the strongest El Niño period. Both the Theilsen method and the Mann–Kendall test showed no significant greening or browning trends over the whole time series, although the annual Mann–Kendall test, in 2003 and 2014–2015, indicated significant browning trends due to the most recent strongest El Niño. Moreover, a multi-linear regression model seems to indicate a significant influence of both ENSO activity and precipitation. Our results indicate that the recent 2014–2016 drought altered the vegetation condition in the HiP. We conclude that it is vital to exploit freely available GEE resources to develop drought monitoring vegetation systems, and to integrate climate information for analyzing its influence on protected areas, especially in data-poor counties.

scholarly journals Phenological Monitoring of Paddy Crop Using Time Series MODIS Data

Proceedings ◽  
2019 ◽  
Vol 24 (1) ◽  
pp. 19
Author(s):  
C. Dineshkumar ◽  
S. Nitheshnirmal ◽  
Ashutosh Bhardwaj ◽  
K. Nivedita Priyadarshini
Keyword(s):  
Time Series ◽  
Tamil Nadu ◽  
Vegetation Index ◽  
Google Earth ◽  
Paddy Fields ◽  
Series Data ◽  
Local Minima ◽  
Phenological Stages ◽  
Local Maxima ◽  
Modis Data

Rice is an important staple food crop worldwide, especially in India. Accurate and timely prediction of rice phenology plays a significant role in the management of water resources, administrative planning, and food security. In addition to conventional methods, remotely sensed time series data can provide the necessary estimation of rice phenological stages over a large region. Thus, the present study utilizes the 16-day composite Enhanced Vegetation Index (EVI) product with a spatial resolution of 250 m from the Moderate Resolution Imaging Spectroradiometer (MODIS) to monitor the rice phenological stages over Karur district of Tamil Nadu, India, using the Google Earth Engine (GEE) platform. The rice fields in the study area were classified using the machine learning algorithm in GEE. The ground truth was obtained from the paddy fields during crop production which was used for classifying the paddy grown area. After the classification of paddy fields, local maxima, and local minima present in each pixel of time series, the EVI product was used to determine the paddy growing stages in the study area. The results show that in the initial stage the pixel value of EVI in the paddy field shows local minima (0.23), whereas local maxima (0.41) were obtained during the peak vegetative stage. The results derived from the present study using MODIS data were cross-validated using the field data.

scholarly journals PHENOLOGICAL CHARACTERIZATION OF COFFEE CROP (Coffea arabica L.) FROM MODIS TIME SERIES

2013 ◽  
Vol 31 (4) ◽  
pp. 569 ◽  
Author(s):  
Antônio Felipe Couto Júnior ◽  
Osmar Abílio de Carvalho Júnior ◽  
Éder De Souza Martins ◽  
Antônio Fernando Guerra
Keyword(s):  
Time Series ◽  
Coffea Arabica ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Median Filter ◽  
Series Data ◽  
Arabica Coffee ◽  
Enhanced Vegetation Index ◽  
Original Dataset ◽  
Coffea Arabica L

ABSTRACT. Arabica Coffee (Coffea arabica L.) demonstrates a two-year phenological cycle, this knowledge is important for crop forecast in Brazil. This work aimed to describe the coffee crop phenology from MODIS vegetation index time series. The study area is located in the western Bahia State, Brazil, due to its remarkable agribusiness development. MODIS time series data comprehended 10-year Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). However, these times series are usually contaminated by noise caused by atmospheric variations that are harmful to the surface discrimination. Median filter and the Minimum Noise Fraction (MNF) were used together to smooth the original dataset. NDVI and EVI temporal profiles showed differences of amplitude and gradient. The results evidenced the Arabica Coffee phenological stages, as described in previous fieldworks. These results showed potential application for large-area land cover monitoring.Keywords: vegetation index, remote sensing, digital image processing. RESUMO. O Café Arábica (Coffea arabica L.) apresenta um ciclo fenológico de dois anos, sendo relevante o seu conhecimento para a previsão de safras no Brasil. O objetivo deste trabalho foi caracterizar a fenologia da cultura de café a partir de séries temporais de índices de vegetação do sensor MODIS. A área de estudo está localizada no oeste do estado da Bahia, Brasil, devido ao seu notável desenvolvimento do agronegócio. As séries temporais MODIS compreendem10 anos do Normalized Difference Vegetation Index (NDVI) e Enhanced Vegetation Index (EVI). Contudo, essas séries temporais apresentam ruídos ocasionados por efeitos atmosféricos queprejudicam a discriminação dos alvos da superfície. O filtro de mediana e a transformação Fração Mínima de Ruído (FMR) foram usados em conjunto para suavizar os dados originais. Os perfis temporais NDVI e EVI apresentam diferenças de amplitude e gradiente. Os resultados evidenciaram os estágios fenológico do Café Arábica, como descritos em prévios trabalhos de campo. Esses resultados possuem potencial de aplicação para o monitoramento do uso da terra em extensas áreas.Palavras-chave: índices de vegetação, sensoriamento remoto, processamento digital de imagem.

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