scholarly journals A Framework of Spatio-Temporal Fusion Algorithm Selection for Landsat NDVI Time Series Construction

2020 ◽  
Vol 9 (11) ◽  
pp. 665
Author(s):  
Yangnan Guo ◽  
Cangjiao Wang ◽  
Shaogang Lei ◽  
Junzhe Yang ◽  
Yibo Zhao
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Linear Relationship ◽  
Vegetation Index ◽  
Input Image ◽  
Fusion Algorithm ◽  
Algorithm Selection ◽  
Temporal Variance ◽  
Ndvi Time Series ◽  
Spatio Temporal

Spatio-temporal fusion algorithms dramatically enhance the application of the Landsat time series. However, each spatio-temporal fusion algorithm has its pros and cons of heterogeneous land cover performance, the minimal number of input image pairs, and its efficiency. This study aimed to answer: (1) how to determine the adaptability of the spatio-temporal fusion algorithm for predicting images in prediction date and (2) whether the Landsat normalized difference vegetation index (NDVI) time series would benefit from the interpolation with images fused from multiple spatio-temporal fusion algorithms. Thus, we supposed a linear relationship existed between the fusion accuracy and spatial and temporal variance. Taking the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and the Enhanced STARFM (ESTARFM) as basic algorithms, a framework was designed to screen a spatio-temporal fusion algorithm for the Landsat NDVI time series construction. The screening rule was designed by fitting the linear relationship between the spatial and temporal variance and fusion algorithm accuracy, and then the fitted relationship was combined with the graded accuracy selecting rule (R2) to select the fusion algorithm. The results indicated that the constructed Landsat NDVI time series by this paper proposed framework exhibited the highest overall accuracy (88.18%), and lowest omission (1.82%) and commission errors (10.00%) in land cover change detection compared with the moderate resolution imaging spectroradiometer (MODIS) NDVI time series and the NDVI time series constructed by a single STARFM or ESTARFM. Phenological stability analysis demonstrated that the Landsat NDVI time series established by multiple spatio-temporal algorithms could effectively avoid phenological fluctuations in the time series constructed by a single fusion algorithm. We believe that this framework can help improve the quality of the Landsat NDVI time series and fulfill the gap between near real-time environmental monitoring mandates and data-scarcity reality.

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

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
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.

scholarly journals Land Cover and Crop Classification Based on Red Edge Indices Features of GF-6 WFV Time Series Data

2021 ◽  
Vol 13 (22) ◽  
pp. 4522
Author(s):  
Yupeng Kang ◽  
Xinli Hu ◽  
Qingyan Meng ◽  
Youfeng Zou ◽  
Linlin Zhang ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Classification Accuracy ◽  
Vegetation Index ◽  
Time Series Data ◽  
Vegetation Indices ◽  
Series Data ◽  
Red Edge ◽  
Ndvi Time Series ◽  
Crop Classification

Time series of vegetation indices can be utilized to capture crop phenology information, and have been widely used in land cover and crop classification, phenological feature extraction, and planting structure monitoring. This is of great significance for guiding agricultural production and formulating agricultural policies. According to the characteristics of the GF-6 satellite’s newly-added red edge bands, wide field view and high-frequency imaging, the time series of vegetation indices about multi-temporal GF-6 WFV data are used for the study of land cover and crop classification. In this study, eight time steps of GF-6 WFV data were selected from March to October 2019 in Hengshui City. The normalized difference vegetation index (NDVI) time series and 10 different red edge spectral indices time series were constructed. Then, based on principal component analysis (PCA), using two feature selection and evaluation methods, stepwise discriminant analysis (SDA) and random forest (RF), the red edge vegetation index of normalized difference red edge (NDRE) was selected. Seven different lengths of NDVI, NDRE and NDVI&NDRE time series were reconstructed by the Savizky-Golay (S-G) smoothing algorithm. Finally, an RF classification algorithm was used to analyze the influence of time series length and red edge indices features on land cover and crop classification, and the planting structure and distribution of crops in the study area were obtained. The results show that: (1) Compared with the NDRE red edge time series, the NDVI time series is more conducive to the improvement of the overall classification accuracy of crops, and NDRE can assist NDVI in improving the crop classification accuracy; (2) With the shortening of NDVI and NDRE time series, the accuracy of crop classification is gradually decreased, and the decline is gradually accelerated; and (3) Through the combination of the NDVI and NDRE time series, the accuracy of crop classification with different time series lengths can be improved compared with the single NDVI time series, which is conducive to improving the classification accuracy and timeliness of crops. This study has fully tapped the application potential of the new red edge bands of GF-6 WFV time series data, which can provide references for crop identification and classification of time series data such as NDVI and red edge vegetation index of different lengths. At the same time, it promotes the application of optical satellite data with red edge bands in the field of agricultural remote sensing.

scholarly journals Time Series Land Cover Mapping and Change Detection Analysis Using Geographic Information System and Remote Sensing, North and West of Africa

Proceedings ◽  
2019 ◽  
Vol 39 (1) ◽  
pp. 3
Author(s):  
Malak Henchiri ◽  
Wilson Kalisa ◽  
Zhang Sha ◽  
Jiahua Zhang
Keyword(s):  
Land Use ◽  
Time Series ◽  
Land Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Temporal Dynamics ◽  
Land Resources ◽  
Detection Analysis ◽  
Spatio Temporal ◽  
Very High

Land use planners require a time series land resources information and changing pattern for future management. Therefore, it is essential to assess changes in land cover. This study was to quantify the spatio-temporal dynamics of land use change over North and West Africa between 1985 and 2015 using the Normalized Difference Vegetation Index (NDVI) from the Very High Resolution Radiometer (AVHRR). The total investigated area was determined by 17,328,557.16 km2. The class of Urban and Built-up, Barren or sparsely vegetated, Savannas and Deciduous Broadleaf Forests increases considerably during the last three decades. In contrast, the class of Open Shrublands, Woody Savannas and water decrease notably during the three decades. The class of croplands decreases from 1985 to 1995 and increased from 1995 to 2015. The class of grasslands recorded a first increase from 1985 to 1995, and then decreased from 1995 to 2015. The class of permanent wetlands first decrease from 1985 to 1995, then increase from 1995 to 2005, followed by a decreasing trend during the last decade. The class of evergreen broadleaf forests decreased in the first two decades, from 1985 to 2005, and increased over the last decade.

scholarly journals THE RECONSTRUCTION OF NDVI TIME SERIES USING SPATIO-TEMPORAL INFORMATION

2020 ◽  
Vol V-3-2020 ◽  
pp. 695-700
Author(s):  
L. Xu ◽  
J. Yang ◽  
S. Li ◽  
X. Li
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Temporal Information ◽  
Modis Ndvi ◽  
Reconstruction Performance ◽  
Ndvi Time Series ◽  
Negative Effect ◽  
Spatio Temporal ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. Due to the influence of cloud cover, atmospheric disturbance and many other factors, normalized difference vegetation index (NDVI) corrupted by noises has a negative effect on the downstream applications. To this end, researchers have developed a large number of methods to reconstruct NDVI time series. The harmonic analysis of time series (HANTS) is one of the most widely used approaches of NDVI reconstruction. In this paper, HANTS algorithm was improved by the utilization of spatio-temporal information of NDVI time series with spatial filling and filtering, which makes up the deficiency of HANTS that only uses temporal information of NDVI time series. The simulation experiments on moderate resolution imaging spectroradiometer (MODIS) NDVI time series have proved that our method has effectively improved the quantitative and qualitative reconstruction performance of HANTS algorithm.

scholarly journals Time Series Analysis of Land Cover Change in Dry Mountains: Insights from the Tajik Pamirs

2021 ◽  
Vol 13 (19) ◽  
pp. 3951
Author(s):  
Kim André Vanselow ◽  
Harald Zandler ◽  
Cyrus Samimi
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Land Cover ◽  
Land Cover Change ◽  
Vegetation Index ◽  
Demographic Data ◽  
Mountainous Areas ◽  
Generalized Additive Mixed Models ◽  
Additive Mixed Models ◽  
Irregular Data

Greening and browning trends in vegetation have been observed in many regions of the world in recent decades. However, few studies focused on dry mountains. Here, we analyze trends of land cover change in the Western Pamirs, Tajikistan. We aim to gain a deeper understanding of these changes and thus improve remote sensing studies in dry mountainous areas. The study area is characterized by a complex set of attributes, making it a prime example for this purpose. We used generalized additive mixed models for the trend estimation of a 32-year Landsat time series (1988–2020) of the modified soil adjusted vegetation index, vegetation data, and environmental and socio-demographic data. With this approach, we were able to cope with the typical challenges that occur in the remote sensing analysis of dry and mountainous areas, including background noise and irregular data. We found that greening and browning trends coexist and that they vary according to the land cover class, topography, and geographical distribution. Greening was detected predominantly in agricultural and forestry areas, indicating direct anthropogenic drivers of change. At other sites, greening corresponds well with increasing temperature. Browning was frequently linked to disastrous events, which are promoted by increasing temperatures.

scholarly journals Spatio-Temporal Mixed Pixel Analysis of Savanna Ecosystems: A Review

2021 ◽  
Vol 13 (19) ◽  
pp. 3870
Author(s):  
Hilma S. Nghiyalwa ◽  
Marcel Urban ◽  
Jussi Baade ◽  
Izak P. J. Smit ◽  
Abel Ramoelo ◽  
...  
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Land Cover ◽  
Parametric Methods ◽  
Mixed Pixel ◽  
Medium Resolution ◽  
Long Time ◽  
Pixel Analysis ◽  
Spatio Temporal ◽  
Resolution Data

Reliable estimates of savanna vegetation constituents (i.e., woody and herbaceous vegetation) are essential as they are both responders and drivers of global change. The savanna is a highly heterogenous biome with high variability in land cover types while also being very dynamic at both temporal and spatial scales. To understand the spatial-temporal dynamics of savannas, using Earth Observation (EO) data for mixed-pixel analysis is crucial. Mixed pixel analysis provides detailed land cover data at a sub-pixel level which are essential for conservation purposes, understanding food supply for herbivores, quantifying environmental change, such as bush encroachment, and fuel availability essential for understanding fire dynamics, and for accurate estimation of savanna biomass. This review paper consulted 197 studies employing mixed-pixel analysis in savanna ecosystems. The review indicates that studies have so far attempted to resolve the savanna mixed-pixel issues by using mainly coarse resolution data, such as Terra-Aqua MODIS and AVHRR and medium resolution Landsat, to provide fractional cover data. Hence, there is a lack of spatio-temporal mixed-pixel analysis for savannas at high spatial resolutions. Methods used for mixed-pixel analysis include parametric and non-parametric methods which range from pixel-unmixing models, such as linear spectral mixture analysis (SMA), time series decomposition, empirical methods to link the green vegetation parameters with Vegetation Indices (VIs), and machine learning methods, such as regression trees (RT) and random forests (RF). Most studies were undertaken at local and regional scale, highlighting a research gap for savanna mixed pixel studies at national, continental, and global level. Parametric methods for modeling spatio-temporal mixed pixel analysis were preferred for coarse to medium resolution remote sensing data, while non-parametric methods were preferred for very high to high spatial resolution data. The review indicates a gap for long time series spatio-temporal mixed-pixel analysis of savannas using high resolution data at various scales. There is potential to harmonize the available low resolution EO data with new high-resolution sensors to provide long time series of the savanna mixed pixel, which, according to this review, is missing.

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

2012 ◽  
Vol 4 (5) ◽  
pp. 897 ◽  
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.

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

2020 ◽  
Vol 12 (14) ◽  
pp. 2195 ◽  
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.

scholarly journals Automatic Land-Cover Mapping using Landsat Time-Series Data based on Google Earth Engine

2019 ◽  
Vol 11 (24) ◽  
pp. 3023 ◽  
Author(s):  
Shuai Xie ◽  
Liangyun Liu ◽  
Xiao Zhang ◽  
Jiangning Yang ◽  
Xidong Chen ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Vegetation Index ◽  
Time Series Data ◽  
Land Cover Classification ◽  
Google Earth ◽  
Thematic Mapper ◽  
Series Data ◽  
Land Cover Mapping ◽  
Google Earth Engine

The Google Earth Engine (GEE) has emerged as an essential cloud-based platform for land-cover classification as it provides massive amounts of multi-source satellite data and high-performance computation service. This paper proposed an automatic land-cover classification method using time-series Landsat data on the GEE cloud-based platform. The Moderate Resolution Imaging Spectroradiometer (MODIS) land-cover products (MCD12Q1.006) with the International Geosphere–Biosphere Program (IGBP) classification scheme were used to provide accurate training samples using the rules of pixel filtering and spectral filtering, which resulted in an overall accuracy (OA) of 99.2%. Two types of spectral–temporal features (percentile composited features and median composited monthly features) generated from all available Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data from the year 2010 ± 1 were used as input features to a Random Forest (RF) classifier for land-cover classification. The results showed that the monthly features outperformed the percentile features, giving an average OA of 80% against 77%. In addition, the monthly features composited using the median outperformed those composited using the maximum Normalized Difference Vegetation Index (NDVI) with an average OA of 80% against 78%. Therefore, the proposed method is able to generate accurate land-cover mapping automatically based on the GEE cloud-based platform, which is promising for regional and global land-cover mapping.

scholarly journals Mapping Evapotranspiration, Vegetation and Precipitation Trends in the Catchment of the Shrinking Lake Poopó

2019 ◽  
Vol 12 (1) ◽  
pp. 73 ◽  
Author(s):  
Juan Torres-Batlló ◽  
Belén Martí-Cardona ◽  
Ramiro Pillco-Zolá
Keyword(s):  
Land Cover ◽  
Vegetation Index ◽  
Temporal Trends ◽  
Temporal Analysis ◽  
Mountain Range ◽  
Water Losses ◽  
Essential Information ◽  
General Decline ◽  
Increasing Trend ◽  
Spatio Temporal

Lake Poopó is located in the Andean Mountain Range Plateau or Altiplano. A general decline in the lake water level has been observed in the last two decades, coinciding roughly with an intensification of agriculture exploitation, such as quinoa crops. Several factors have been linked with the shrinkage of the lake, including climate change, increased irrigation, mining extraction and population growth. Being an endorheic catchment, evapotranspiration (ET) losses are expected to be the main water output mechanism and previous studies demonstrated ET increases using Earth observation (EO) data. In this study, we seek to build upon these earlier findings by analyzing an ET time series dataset of higher spatial and temporal resolution, in conjunction with land cover and precipitation data. More specifically, we performed a spatio-temporal analysis, focusing on wet and dry periods, that showed that ET changes occur primarily in the wet period, while the dry period is approximately stationary. An analysis of vegetation trends performed using 500 MODIS vegetation index products (NDVI) also showed an overall increasing trend during the wet period. Analysis of NDVI and ET across land cover types showed that only croplands had experienced an increase in NDVI and ET losses, while natural covers showed either constant or decreasing NDVI trends together with increases in ET. The larger increase in vegetation and ET losses over agricultural regions, strongly suggests that cropping practices exacerbated water losses in these areas. This quantification provides essential information for the sustainable planning of water resources and land uses in the catchment. Finally, we examined the spatio-temporal trends of the precipitation using the newly available Climate Hazards Group Infrared Precipitation with Stations (CHIRPS-v2) product, which we validated with onsite rainfall measurements. When integrated over the entire catchment, precipitation and ET showed an average increasing trend of 5.2 mm yr−1 and 4.3 mm yr−1, respectively. This result suggests that, despite the increased ET losses, the catchment-wide water storage should have been offset by the higher precipitation. However, this result is only applicable to the catchment-wide water balance, and the location of water may have been altered (e.g., by river abstractions or by the creation of impoundments) to the detriment of the Lake Poopó downstream.

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