Mapping Periodic Patterns of Global Vegetation Based on Spectral Analysis of NDVI Time Series

Vegetation seasonality assessment through remote sensing data is crucial to understand ecosystem responses to climatic variations and human activities at large-scales. Whereas the study of the timing of phenological events showed significant advances, their recurrence patterns at different periodicities has not been widely study, especially at global scale. In this work, we describe vegetation oscillations by a novel quantitative approach based on the spectral analysis of Normalized Difference Vegetation Index (NDVI) time series. A new set of global periodicity indicators permitted to identify different seasonal patterns regarding the intra-annual cycles (the number, amplitude, and stability) and to evaluate the existence of pluri-annual cycles, even in those regions with noisy or low NDVI. Most of vegetated land surface (93.18%) showed one intra-annual cycle whereas double and triple cycles were found in 5.58% of the land surface, mainly in tropical and arid regions along with agricultural areas. In only 1.24% of the pixels, the seasonality was not statistically significant. The highest values of amplitude and stability were found at high latitudes in the northern hemisphere whereas lowest values corresponded to tropical and arid regions, with the latter showing more pluri-annual cycles. The indicator maps compiled in this work provide highly relevant and practical information to advance in assessing global vegetation dynamics in the context of global change.

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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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Assessing Spatial Limits of Sentinel-2 Data on Arable Crops in the Context of Checks by Monitoring

Remote Sensing ◽

10.3390/rs12142195 ◽

2020 ◽

Vol 12 (14) ◽

pp. 2195 ◽

Cited By ~ 4

Author(s):

Blanka Vajsová ◽

Dominique Fasbender ◽

Csaba Wirnhardt ◽

Slavko Lemajic ◽

Wim Devos

Keyword(s):

Time Series ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Policy Framework ◽

Pixel Size ◽

Control Approach ◽

Continuous State ◽

Ndvi Time Series ◽

High Level ◽

Sentinel 2

The availability of large amounts of Sentinel-2 data has been a trigger for its increasing exploitation in various types of applications. It is, therefore, of importance to understand the limits above which these data still guarantee a meaningful outcome. This paper proposes a new method to quantify and specify restrictions of the Sentinel-2 imagery in the context of checks by monitoring, a newly introduced control approach within the European Common Agriculture Policy framework. The method consists of a comparison of normalized difference vegetation index (NDVI) time series constructed from data of different spatial resolution to estimate the performance and limits of the coarser one. Using similarity assessment of Sentinel-2 (10 m pixel size) and PlanetScope (3 m pixel size) NDVI time series, it was estimated that for 10% out of 867 fields less than 0.5 ha in size, Sentinel-2 data did not provide reliable evidence of the activity or state of the agriculture field over a given timeframe. Statistical analysis revealed that the number of clean or full pixels and the proportion of pixels lost after an application of a 5-m (1/2 pixel) negative buffer are the geospatial parameters of the field that have the highest influence on the ability of the Sentinel-2 data to qualify the field’s state in time. We specified the following limiting criteria: at least 8 full pixels inside a border and less than 60% of pixels lost. It was concluded that compliance with the criteria still assures a high level of extracted information reliability. Our research proved the promising potential, which was higher than anticipated, of Sentinel-2 data for the continuous state assessment of small fields. The method could be applied to other sensors and indicators.

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Estimation of different data compositions for early-season crop type classification

PeerJ ◽

10.7717/peerj.4834 ◽

2018 ◽

Vol 6 ◽

pp. e4834 ◽

Cited By ~ 10

Author(s):

Pengyu Hao ◽

Mingquan Wu ◽

Zheng Niu ◽

Li Wang ◽

Yulin Zhan

Keyword(s):

Time Series ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Remote Sensing Data ◽

Classification Performance ◽

Distribution Maps ◽

Production Forecasting ◽

Crop Type ◽

Ndvi Time Series ◽

Multi Temporal

Timely and accurate crop type distribution maps are an important inputs for crop yield estimation and production forecasting as multi-temporal images can observe phenological differences among crops. Therefore, time series remote sensing data are essential for crop type mapping, and image composition has commonly been used to improve the quality of the image time series. However, the optimal composition period is unclear as long composition periods (such as compositions lasting half a year) are less informative and short composition periods lead to information redundancy and missing pixels. In this study, we initially acquired daily 30 m Normalized Difference Vegetation Index (NDVI) time series by fusing MODIS, Landsat, Gaofen and Huanjing (HJ) NDVI, and then composited the NDVI time series using four strategies (daily, 8-day, 16-day, and 32-day). We used Random Forest to identify crop types and evaluated the classification performances of the NDVI time series generated from four composition strategies in two studies regions from Xinjiang, China. Results indicated that crop classification performance improved as crop separabilities and classification accuracies increased, and classification uncertainties dropped in the green-up stage of the crops. When using daily NDVI time series, overall accuracies saturated at 113-day and 116-day in Bole and Luntai, and the saturated overall accuracies (OAs) were 86.13% and 91.89%, respectively. Cotton could be identified 40∼60 days and 35∼45 days earlier than the harvest in Bole and Luntai when using daily, 8-day and 16-day composition NDVI time series since both producer’s accuracies (PAs) and user’s accuracies (UAs) were higher than 85%. Among the four compositions, the daily NDVI time series generated the highest classification accuracies. Although the 8-day, 16-day and 32-day compositions had similar saturated overall accuracies (around 85% in Bole and 83% in Luntai), the 8-day and 16-day compositions achieved these accuracies around 155-day in Bole and 133-day in Luntai, which were earlier than the 32-day composition (170-day in both Bole and Luntai). Therefore, when the daily NDVI time series cannot be acquired, the 16-day composition is recommended in this study.

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Land Cover Information Extraction Based on Daily NDVI Time Series and Multiclassifier Combination

Mathematical Problems in Engineering ◽

10.1155/2017/6824051 ◽

2017 ◽

Vol 2017 ◽

pp. 1-13 ◽

Cited By ~ 1

Author(s):

Long Zhao ◽

Pan Zhang ◽

Xiaoyi Ma ◽

Zhuokun Pan

Keyword(s):

Time Series ◽

Land Cover ◽

Information Extraction ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Classification And Regression Tree ◽

Support Vector ◽

Land Resource ◽

Spectral Angle Mapper ◽

Ndvi Time Series

A timely and accurate understanding of land cover change has great significance in management of area resources. To explore the application of a daily normalized difference vegetation index (NDVI) time series in land cover classification, the present study used HJ-1 data to derive a daily NDVI time series by pretreatment. Different classifiers were then applied to classify the daily NDVI time series. Finally, the daily NDVI time series were classified based on multiclassifier combination. The results indicate that support vector machine (SVM), spectral angle mapper, and classification and regression tree classifiers can be used to classify daily NDVI time series, with SVM providing the optimal classification. The classifiers of K-means and Mahalanobis distance are not suited for classification because of their classification accuracy and mechanism, respectively. This study proposes a method of dimensionality reduction based on the statistical features of daily NDVI time series for classification. The method can be applied to land resource information extraction. In addition, an improved multiclassifier combination is proposed. The classification results indicate that the improved multiclassifier combination is superior to different single classifier combinations, particularly regarding subclassifiers with greater differences.

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Comparison between the Holt-Winters and SARIMA Models in the Prediction of NDVI in an Arid Region in Kenya using Pixel-wise NDVI Time Series

Academic Journal of Research and Scientific Publishing ◽

10.52132/ajrsp/en.2231 ◽

2021 ◽

Vol 2 (23) ◽

pp. 1-15

Author(s):

Mwana Said Omar ◽

◽

Hajime Kawamukai

Keyword(s):

Time Series ◽

Arid Region ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Moving Average ◽

Additive Model ◽

Grid Search ◽

Sarima Model ◽

Ndvi Time Series ◽

Sarima Models

Desertification is major issue in arid and semi-arid lands (ASAL) with devastating environmental and socio-economic impacts. Time series analysis was applied on 19 years’ pixel-wise monthly mean Normalized Difference Vegetation Index (NDVI) data. The aim of this study was to identify a time series model that can be used to predict NDVI at the pixel level in an arid region in Kenya. The Holt-Winters and Seasonal Auto Regressive Integrated Moving Average (SARIMA) models were developed and statistical analysis was carried out using both models on the study area. We performed a grid search to optimise and determine the best hyper parameters for the models. Results from the grid search identified the Holt-Winters model as an additive model and a SARIMA model with a trend autoregressive (AR) order of 1, a trend moving average (MA) order of 1 and a seasonal MA order of 2, with both models having a seasonal period of 12 months. It was concluded that the Holt-Winters model showed the best performance for 600 ✕ 600 pixels (MAE = 0.0744, RMSE = 0.096) compared to the SARIMA model.

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Influence of Varying Solar Zenith Angles on Land Surface Phenology Derived from Vegetation Indices: A Case Study in the Harvard Forest

Remote Sensing ◽

10.3390/rs13204126 ◽

2021 ◽

Vol 13 (20) ◽

pp. 4126

Author(s):

Yang Li ◽

Ziti Jiao ◽

Kaiguang Zhao ◽

Yadong Dong ◽

Yuyu Zhou ◽

...

Keyword(s):

Time Series ◽

Land Surface ◽

Vegetation Index ◽

Vegetation Indices ◽

Mean Difference ◽

Land Surface Phenology ◽

Harvard Forest ◽

Ndvi Time Series ◽

Bidirectional Reflectance Distribution

Vegetation indices are widely used to derive land surface phenology (LSP). However, due to inconsistent illumination geometries, reflectance varies with solar zenith angles (SZA), which in turn affects the vegetation indices, and thus the derived LSP. To examine the SZA effect on LSP, the MODIS bidirectional reflectance distribution function (BRDF) product and a BRDF model were employed to derive LSPs under several constant SZAs (i.e., 0°, 15°, 30°, 45°, and 60°) in the Harvard Forest, Massachusetts, USA. The LSPs derived under varying SZAs from the MODIS nadir BRDF-adjusted reflectance (NBAR) and MODIS vegetation index products were used as baselines. The results show that with increasing SZA, NDVI increases but EVI decreases. The magnitude of SZA-induced NDVI/EVI changes suggests that EVI is more sensitive to varying SZAs than NDVI. NDVI and EVI are comparable in deriving the start of season (SOS), but EVI is more accurate when deriving the end of season (EOS). Specifically, NDVI/EVI-derived SOSs are relatively close to those derived from ground measurements, with an absolute mean difference of 8.01 days for NDVI-derived SOSs and 9.07 days for EVI-derived SOSs over ten years. However, a considerable lag exists for EOSs derived from vegetation indices, especially from the NDVI time series, with an absolute mean difference of 14.67 days relative to that derived from ground measurements. The SOSs derived from NDVI time series are generally earlier, while those from EVI time series are delayed. In contrast, the EOSs derived from NDVI time series are delayed; those derived from the simulated EVI time series under a fixed illumination geometry are also delayed, but those derived from the products with varying illumination geometries (i.e., MODIS NBAR product and MODIS vegetation index product) are advanced. LSPs derived from varying illumination geometries could lead to a difference spanning from a few days to a month in this case study, which highlights the importance of normalizing the illumination geometry when deriving LSP from NDVI/EVI time series.

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Monitoring Broadscale Vegetational Diversity and Change across North American Landscapes Using Land Surface Phenology

Forests ◽

10.3390/f11060606 ◽

2020 ◽

Vol 11 (6) ◽

pp. 606

Author(s):

Bjorn-Gustaf J. Brooks ◽

Danny C. Lee ◽

Lars Y. Pomara ◽

William W. Hargrove

Keyword(s):

Time Series ◽

Factor Analysis ◽

Coordinate Transformation ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Ecological Gradients ◽

Land Surface Phenology ◽

Polar Coordinate ◽

Vegetational Diversity

We describe a polar coordinate transformation of vegetation index profiles which permits a broad-scale comparison of location-specific phenological variability influenced by climate, topography, land use, and other factors. We apply statistical data reduction techniques to identify fundamental dimensions of phenological variability and to classify phenological types with intuitive ecological interpretation. Remote sensing-based land surface phenology can reveal ecologically meaningful vegetational diversity and dynamics across broad landscapes. Land surface phenology is inherently complex at regional to continental scales, varying with latitude, elevation, and multiple biophysical factors. Quantifying phenological change across ecological gradients at these scales is a potentially powerful way to monitor ecological development, disturbance, and diversity. Polar coordinate transformation was applied to Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) time series spanning 2000-2018 across North America. In a first step, 46 NDVI values per year were reduced to 11 intuitive annual metrics, such as the midpoint of the growing season and degree of seasonality, measured relative to location-specific annual phenological cycles. Second, factor analysis further reduced these metrics to fundamental phenology dimensions corresponding to annual timing, productivity, and seasonality. The factor analysis explained over 95% of the variability in the metrics and represented a more than ten-fold reduction in data volume from the original time series. In a final step, phenological classes (‘phenoclasses’) based on the statistical clustering of the factor data, were computed to describe the phenological state of each pixel during each year, which facilitated the tracking of year-to-year dynamics. Collectively the phenology metrics, factors, and phenoclasses provide a system for characterizing land surface phenology and for monitoring phenological change that is indicative of ecological gradients, development, disturbance, and other aspects of landscape-scale diversity and dynamics.

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Using OCO-2 Satellite Data for Investigating the Variability of Atmospheric CO2 Concentration in Relationship with Precipitation, Relative Humidity, and Vegetation over Oman

Water ◽

10.3390/w12010101 ◽

2019 ◽

Vol 12 (1) ◽

pp. 101

Author(s):

Foroogh Golkar ◽

Malik Al-Wardy ◽

Seyedeh Fatemeh Saffari ◽

Kathiya Al-Aufi ◽

Ghazi Al-Rawas

Keyword(s):

Time Series ◽

Relative Humidity ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Co2 Concentration ◽

Atmospheric Co2 ◽

Monsoon Region ◽

Annual Cycles ◽

Atmospheric Co2 Concentration ◽

Carbon Dioxide Co2

Recognition of the carbon dioxide (CO2) concentration variations over time is critical for tracing the future changes in climate both globally and regionally. In this study, a time series analysis of atmospheric CO2 concentration and its relationship with precipitation, relative humidity (RH), and vegetation is investigated over Oman. The daily XCO2 data from OCO-2 satellite was obtained from September 2014 to March 2019. The daily RH and precipitation data were also collected from the ground weather stations, and the Normalized Difference Vegetation Index was obtained from MODIS. Oman was studied in four distinct regions where the main emphasis was on the Monsoon Region in the far south. The CO2 concentration time series indicated a significant upward trend over different regions for the study period, with annual cycles being the same for all regions except the Monsoon Region. This is indicative of RH, precipitation, and consequently vegetation cover impact on atmospheric CO2 concentration, resulting in an overall lower annual growth in the Monsoon Region. Simple and multiple correlation analyses of CO2 concentration with mentioned parameters were performed in zero to three-month lags over Oman. They showed high correlations mainly during the rainfall period in the Monsoon Region.

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Prediction of Crop Yield Using Phenological Information Extracted from Remote Sensing Vegetation Index

Sensors ◽

10.3390/s21041406 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1406

Author(s):

Zhonglin Ji ◽

Yaozhong Pan ◽

Xiufang Zhu ◽

Jinyun Wang ◽

Qiannan Li

Keyword(s):

Remote Sensing ◽

Time Series ◽

Arid Regions ◽

Vegetation Index ◽

Corn Yield ◽

Yield Prediction ◽

Multivariate Models ◽

Ndvi Time Series ◽

Phenological Metrics ◽

Semi Arid

Phenology is an indicator of crop growth conditions, and is correlated with crop yields. In this study, a phenological approach based on a remote sensing vegetation index was explored to predict the yield in 314 counties within the US Corn Belt, divided into semi-arid and non-semi-arid regions. The Moderate Resolution Imaging Spectroradiometer (MODIS) data product MOD09Q1 was used to calculate the normalized difference vegetation index (NDVI) time series. According to the NDVI time series, we divided the corn growing season into four growth phases, calculated phenological information metrics (duration and rate) for each growth phase, and obtained the maximum correlation NDVI (Max-R2). Duration and rate represent crop growth days and rate, respectively. Max-R2 is the NDVI value with the most significant correlation with corn yield in the NDVI time series. We built three groups of yield regression models, including univariate models using phenological metrics and Max-R2, and multivariate models using phenological metrics, and multivariate models using phenological metrics combined with Max-R2 in the whole, semi-arid, and non-semi-arid regions, respectively, and compared the performance of these models. The results show that most phenological metrics had a statistically significant (p < 0.05) relationship with corn yield (maximum R2 = 0.44). Models established with phenological metrics realized yield prediction before harvest in the three regions with R2 = 0.64, 0.67, and 0.72. Compared with the univariate Max-R2 models, the accuracy of models built with Max-R2 and phenology metrics improved. Thus, the phenology metrics obtained from MODIS-NDVI accurately reflect the corn characteristics and can be used for large-scale yield prediction. Overall, this study showed that phenology metrics derived from remote sensing vegetation indexes could be used as crop yield prediction variables and provide a reference for data organization and yield prediction with physical crop significance.

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