scholarly journals ФРАКТАЛЬНИЙ АНАЛІЗ КОСМІЧНИХ ЗНІМКІВ SENTINEL-2 ДЛЯ МОНІТОРИНГУ СІЛЬСЬКОГОСПОДАРСЬКИХ КУЛЬТУР

2020 ◽  
pp. 34-47
Author(s):  
Максим В’ячеславович Марюшко ◽  
Руслан Едуардович Пащенко
Keyword(s):  
Time Series ◽  
Satellite Imagery ◽  
Fractal Analysis ◽  
Near Infrared ◽  
Fractal Dimensions ◽  
Window Size ◽  
Sliding Window ◽  
Underlying Surface ◽  
Ndvi Index ◽  
Sentinel 2

The subject of the study in the article is using the new approach to the processing of spatial information from satellites for more effective and operational evaluation of crops. This is due to the growing trend of access to remote sensing data, due to the improvement of spatial and temporal resolution, which can be used in the analysis of vegetation cover and other related work. The goal of the article is the capability assessment of processing the Sentinel-2 satellite imagery using fractal dimensions to agricultural plant monitoring at different phases of the vegetative. The tasks: to research the method of constructing fractal dimensions for the Sentinel-2 satellite imagery to assess the state of crops during the vegetative phase; to assess the relationship between changes in FD averages and changes in the NDVI index of different time series remote images, to determine the advantage of calculation method fractal dimensions compared to the NDVI index. The following results were obtained. It was found that the NDVI index is most often used to quantify the state of biomass during different time intervals. But this index becomes ineffective during periods of weakening of the vegetation active phase. Accordingly, it is of practical interest to evaluate the possibility of using fractal analysis of agricultural crop satellite imagery at different vegetation phases. The basis of fractal analysis of digital images is the formation of fractal dimensions fields. The analysis of changes in the FD values on different remote images time series of the grain cornfields from the «sliding window» values is carried out. The dependences of the maximum and minimum values of FD, which are in the images, on the «window» size are investigated. It is shown that the homogeneity of the underlying surface can be estimated from the magnitude of changes in the maximum values of FD with the increasing size of the «window». It is established that the pattern of the change of the FD minimum values when changing the «window» size is due to the large sharpness of the underlying surface in the images, and the anomalous behavior of these values allows determining anomalous areas of different sizes in satellite imagery. The pattern of the change in the range of FD with increasing size of the «window», which can be used to determine the homogeneity of the underlying surface in satellite imagery, as well as during the detection of abnormal areas on them. The change analysis of FD average values with an increase in the sizes of «sliding window» is carried out. It is shown that with the same size of the «window» for different image time series, the average FD will be different, which can be used to characterize the agriculture crop vegetation phase. It is established that the pattern of changes in the FD average values is the same as the NDVI indices for different satellite imagery time series of the corn crop fields and that the magnitudes of the FD average values depend on the size of the «window». The size of the «window» is recommended, which provides accommodation between the speed of image processing and the quality of the assessment state vegetation crop. It is shown that to increase the speed of formation of the FFD during the processing of large images, it is advisable to use a «jumping window» instead of a «sliding window». It is mentioned that the «jump» value can be equal to the «window» size. This «jump» value provides maximum speed and does not affect the crop satellite imagery processing quality. Conclusions. The recommended approach to the processing of spatial data from satellites allows assessing the crops' consistency using FD. The pattern of the change in the FD mean values is identical to the NDVI change in different satellite imagery time series of corn crops. In that event, when forming the FFD, data from only one channel of the Sentinel-2 satellite can be used (for example, from the near-infrared channel – b8), and to calculate the NDVI index it is necessary to obtain data from two channels (from the near-infrared and red channels – channels b8 and b4 of the satellite Sentinel-2, respectively), which will reduce the processing time. The scale of FD average values allows detecting a qualitative change in biomass. During further research, it is advisable to perform fractal analysis of Sentinel-2 satellite imagery for other crops at different phases of the vegetation.

scholarly journals A development of the Enhanced Built-up and Bareness Index (EBBI) based on combination of multi-resolution Landsat 8 and Sentinel 2 MSI images

2021 ◽  
Vol 62 (1) ◽  
pp. 1-9
Author(s):  
Hung Le Trinh ◽  
Ha Thu Thi Le ◽  
Loc Duc Le ◽  
Long Thanh Nguyen ◽  
Keyword(s):  
Land Cover ◽  
Satellite Imagery ◽  
Near Infrared ◽  
Urban Land ◽  
Urban Land Use ◽  
Landsat 8 ◽  
Infrared Band ◽  
Bare Land ◽  
Sentinel 2

Classification of built-up land and bare land on remote sensing images is a very difficult problem due to the complexity of the urban land cover. Several urban indices have been proposed to improve the accuracy in classifying urban land use/land cover from optical satellite imagery. This paper presents an development of the EBBI (Enhanced Built-up and Bareness Index) index based on the combination of Landsat 8 and Sentinel 2 multi-resolution satellite imagery. Near infrared band (band 8a), short wave infrared band (band 11) of Sentinel 2 MSI image and thermal infrared band (band 10) Landsat 8 image were used to calculate EBBI index. The results obtained show that the combination of Landsat 8 and Sentinel 2 satellite images improves the spatial resolution of EBBI index image, thereby improving the accuracy of classification of bare land and built-up land by about 5% compared with the case using only Landsat 8 images.

scholarly journals Understanding deep learning in land use classification based on Sentinel-2 time series

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Manuel Campos-Taberner ◽  
Francisco Javier García-Haro ◽  
Beatriz Martínez ◽  
Emma Izquierdo-Verdiguier ◽  
Clement Atzberger ◽  
...  
Keyword(s):  
Neural Network ◽  
Land Use ◽  
Time Series ◽  
Deep Learning ◽  
Agricultural Policy ◽  
Near Infrared ◽  
Land Use Classification ◽  
Policy Compliance ◽  
Economic Return ◽  
Sentinel 2

Abstract The use of deep learning (DL) approaches for the analysis of remote sensing (RS) data is rapidly increasing. DL techniques have provided excellent results in applications ranging from parameter estimation to image classification and anomaly detection. Although the vast majority of studies report precision indicators, there is a lack of studies dealing with the interpretability of the predictions. This shortcoming hampers a wider adoption of DL approaches by a wider users community, as model’s decisions are not accountable. In applications that involve the management of public budgets or policy compliance, a better interpretability of predictions is strictly required. This work aims to deepen the understanding of a recurrent neural network for land use classification based on Sentinel-2 time series in the context of the European Common Agricultural Policy (CAP). This permits to address the relevance of predictors in the classification process leading to an improved understanding of the behaviour of the network. The conducted analysis demonstrates that the red and near infrared Sentinel-2 bands convey the most useful information. With respect to the temporal information, the features derived from summer acquisitions were the most influential. These results contribute to the understanding of models used for decision making in the CAP to accomplish the European Green Deal (EGD) designed in order to counteract climate change, to protect biodiversity and ecosystems, and to ensure a fair economic return for farmers.

scholarly journals IMPROVING LULC CLASSIFICATION FROM SATELLITE IMAGERY USING DEEP LEARNING – EUROSAT DATASET

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 369-376
Author(s):  
H. Yassine ◽  
K. Tout ◽  
M. Jaber
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Satellite Imagery ◽  
Vegetation Index ◽  
Near Infrared ◽  
Spectral Feature ◽  
Classification Performance ◽  
Training Data ◽  
Data Set ◽  
Sentinel 2

Abstract. Machine learning (ML) has proven useful for a very large number of applications in several domains. It has realized a remarkable growth in remote-sensing image analysis over the past few years. Deep Learning (DL) a subset of machine learning were applied in this work to achieve a better classification of Land Use Land Cover (LULC) in satellite imagery using Convolutional Neural Networks (CNNs). EuroSAT benchmarking data set is used as training data set which uses Sentinel-2 satellite images. Sentinel-2 provides images with 13 spectral feature bands, but surprisingly little attention has been paid to these features in deep learning models. The majority of applications focused only on using RGB due to high availability of the RGB models in computer vision. While RGB gives an accuracy of 96.83% using CNN, we are presenting two approaches to improve the classification performance of Sentinel-2 images. In the first approach, features are extracted from 13 spectral feature bands of Sentinel-2 instead of RGB which leads to accuracy of 98.78%. In the second approach features are extracted from 13 spectral bands of Sentinel-2 in addition to calculated indices used in LULC like Blue Ratio (BR), Vegetation index based on Red Edge (VIRE) and Normalized Near Infrared (NNIR), etc. which gives a better accuracy of 99.58%.

scholarly journals OPTIMASI PREDIKSI INFLASI DENGAN NEURAL NETWORK PADA TAHAP WINDOWING ADAKAH PENGARUH PERBEDAAN WINDOW SIZE

2021 ◽  
Vol 12 (3) ◽  
pp. 176
Author(s):  
Rizchi Eka Wahyuni
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Window Size ◽  
Sliding Window ◽  
Backpropagation Neural Network ◽  
Cross Sectional

AbstrakInflasi adalah indikator yang penting dalam penentuan kebijakan pemerintah. Data inflasi dirilis oleh Badan Pusat Statistik (BPS) di setiap awal bulan. Jika data inflasi dapat diprediksi lebih awal, pemerintah bisa menerapkan kebijakan yang tepat. Backpropagation neural network adalah salah satu metode prediksi yang lazim digunakan. Dengan menggunakan data bulan-bulan sebelumnya, inflasi dapat diprediksi menggunakan metode neural network dengan menggunakan teknik sliding window yang juga disebut metode windowing. Windowing adalah pembentukan struktur dari data time series menjadi data cross sectional. Ukuran dari windowing akan mempengaruhi akurasi dari hasil prediksi. Pada penelitian ini, penulis melakukan percobaan dengan tiga window size yaitu 6, 12, dan 18 untuk melihat adakah perbedaan akurasi hasil dari beberapa window size tersebut. Hasil percobaan menyimpulkan bahwa window size 6 memiliki akurasi paling baik untuk memprediksi inflasi dengan RMSE 0,435.Keywords: backpropagation, prediksi, sliding window

Fire regimes and post-fire evolution of burned areas in selected plant biomes of the planet studying the phenology of the landscape with time series of satellite images

2021 ◽  
Author(s):  
Nikos Koutsias ◽  
Anastasia Karamitsou ◽  
Foula Nioti ◽  
Frank Coutelieris
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Satellite Imagery ◽  
Satellite Images ◽  
Fire History ◽  
Fire Regime ◽  
Fire Regimes ◽  
Burned Areas ◽  
History Of ◽  
Sentinel 2

<p>Plant biomes and climatic zones are characterized by a specific type of fire regime which can be determined from the history of fires in the area and it is a synergy mainly of the climatic conditions and the functional characteristics of the types of vegetation. They correspond also to specific phenology types, a feature that can be useful for various applications related to vegetation monitoring, especially when remote sensing methods are used. Both the assessment of fire regime from the reconstruction of fire history and the monitoring of post-fire evolution of the burned areas can be studied with satellite remote sensing based on satellite time series images. The free availability of (i) Landsat satellite imagery by US Geological Survey (USGS, (ii) Sentinel-2 satellite imagery by ESA and (iii) MODIS satellite imagery by NASA / USGS allow low-cost data acquisition and processing (eg 1984-present) which otherwise would require very high costs. The purpose of this work is to determine the fire regime as well as the patterns of post-fire evolution of burned areas in selected vegetation/climate zones for the entire planet by studying the phenology of the landscape with time series of satellite images. More specifically, the three research questions we are negotiating are: (i) the reconstruction of the history of fires in the period 1984-2017 and the determination of fire regimes with Landsat and Sentinel-2 satellite data , (ii) the assessment of pre-fire phenological pattern of vegetation and (iii) the monitoring and comparative evaluation of post-fire evolution patterns of the burned areas.</p><p><strong>Acknowledgements</strong></p><p>This research has been co-financed by the Operational Program "Human Resources Development, Education and Lifelong Learning" and is co-financed by the European Union (European Social Fund) and Greek national funds.</p><p> </p>

scholarly journals Developing Transformation Functions for VENμS and Sentinel-2 Surface Reflectance over Israel

2019 ◽  
Vol 11 (14) ◽  
pp. 1710 ◽  
Author(s):  
V.S. Manivasagam ◽  
Gregoriy Kaplan ◽  
Offer Rozenstein
Keyword(s):  
Time Series ◽  
Near Infrared ◽  
Transformation Model ◽  
Absolute Difference ◽  
Surface Reflectance ◽  
Vegetation Monitoring ◽  
Blue Band ◽  
Spectral Angle Mapper ◽  
Transformation Functions ◽  
Sentinel 2

Vegetation and Environmental New micro Spacecraft (VENμS) and Sentinel-2 are both ongoing earth observation missions that provide high-resolution multispectral imagery at 10 m (VENμS) and 10–20 m (Sentinel-2), at relatively high revisit frequencies (two days for VENμS and five days for Sentinel-2). Sentinel-2 provides global coverage, whereas VENμS covers selected regions, including parts of Israel. To facilitate the combination of these sensors into a unified time-series, a transformation model between them was developed using imagery from the region of interest. For this purpose, same-day acquisitions from both sensor types covering the surface reflectance over Israel, between April 2018 and November 2018, were used in this study. Transformation coefficients from VENμS to Sentinel-2 surface reflectance were produced for their overlapping spectral bands (i.e., visible, red-edge and near-infrared). The performance of these spectral transformation functions was assessed using several methods, including orthogonal distance regression (ODR), the mean absolute difference (MAD), and spectral angle mapper (SAM). Post-transformation, the value of the ODR slopes were close to unity for the transformed VENμS reflectance with Sentinel-2 reflectance, which indicates near-identity of the two datasets following the removal of systemic bias. In addition, the transformation outputs showed better spectral similarity compared to the original images, as indicated by the decrease in SAM from 0.093 to 0.071. Similarly, the MAD was reduced post-transformation in all bands (e.g., the blue band MAD decreased from 0.0238 to 0.0186, and in the NIR it decreased from 0.0491 to 0.0386). Thus, the model helps to combine the images from Sentinel-2 and VENμS into one time-series that facilitates continuous, temporally dense vegetation monitoring.

scholarly journals Fractal analysis of the ULF geomagnetic data obtained at Izu Peninsula, Japan in relation to the nearby earthquake swarm of June–August 2000

2003 ◽  
Vol 3 (3/4) ◽  
pp. 229-236 ◽  
Author(s):  
K. Gotoh ◽  
M. Hayakawa ◽  
N. Smirnova
Keyword(s):  
Time Series ◽  
Fractal Dimension ◽  
Active Region ◽  
Fractal Analysis ◽  
Flicker Noise ◽  
Earthquake Swarm ◽  
Fractal Dimensions ◽  
Izu Peninsula ◽  
Geomagnetic Data ◽  
Fractal Characteristics

Abstract. In our recent papers we applied fractal methods to extract the earthquake precursory signatures from scaling characteristics of the ULF geomagnetic data, obtained in a seismic active region of Guam Island during the large earthquake of 8 August 1993. We found specific dynamics of their fractal characteristics (spectral exponents and fractal dimensions) before the earthquake: appearance of the flicker-noise signatures and increase of the time series fractal dimension. Here we analyze ULF geomagnetic data obtained in a seismic active region of Izu Peninsula, Japan during a swarm of the strong nearby earthquakes of June–August 2000 and compare the results obtained in both regions. We apply the same methodology of data processing using the FFT procedure, Higuchi method and Burlaga-Klein approach to calculate the spectral exponents and fractal dimensions of the ULF time series. We found the common features and specific peculiarities in the behavior of fractal characteristics of the ULF time series before Izu and Guam earthquakes. As a common feature, we obtained the same increase of the ULF time series fractal dimension before the earthquakes, and as specific peculiarity – this increase appears to be sharp for Izu earthquake in comparison with gradual increase of the ULF time series fractal dimension for Guam earthquake. The results obtained in both regions are discussed on the basis of the SOC (self-organized criticality) concept taking into account the differences in the depths of the earthquake focuses. On the basis of the peculiarities revealed, we advance methodology for extraction of the earthquake precursory signatures. As an adjacent step, we suggest the combined analysis of the ULF time series in the parametric space polarization ratio – fractal dimension. We reason also upon the advantage of the multifractal approach with respect to the mono-fractal analysis for study of the earthquake preparation dynamics.

scholarly journals Detecting land cover change using Sentinel-2

2019 ◽  
Vol 1 ◽  
pp. 1-1
Author(s):  
Koji Osumi
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Satellite Imagery ◽  
Near Infrared ◽  
European Space Agency ◽  
Open Data ◽  
Land Cover Changes ◽  
Space Agency ◽  
The Difference ◽  
Sentinel 2

<p><strong>Abstract.</strong> As many studies which detect land cover changes using satellite imagery have been conducted previously; this study uses satellite imagery from Sentinel-2, which was launched by European Space Agency (ESA) in 2015. The main characteristics of Sentinel-2 are: a 10&amp;thinsp;m spatial resolution in visible and Near-infrared (NIR) bands, a revisit frequency of 5 days based on combining Sentinel-2A and Sentinel-2B, and a free and open data policy. Using bands 4 and 8 of Sentinel-2, NDVI is calculated to assess whether the target being observed contains live green vegetation. The difference was calculated by subtracting NDVI of one day from another. Changes from vegetation to built-up areas can be detected via the changes in NDVI. However, automatically computing land cover changes generates errors under present circumstances. In order to detect land cover change accurately, human review is required. This study focuses on how NDVI can assist analysts in quantifying land cover change. As a result of the analysis, land cover changes were extracted by differencing NDVI images of 2 periods, but some errors arose in the places where land cover did not change but NDVI fluctuated owing to other reasons. I show the land cover changes which were detected, the places where it is difficult to detect the change, and methods to reduce the errors. Abstracts</p>

scholarly journals An Improved Spatial and Temporal Reflectance Unmixing Model to Synthesize Time Series of Landsat-Like Images

2018 ◽  
Vol 10 (9) ◽  
pp. 1388 ◽  
Author(s):  
Jianhang Ma ◽  
Wenjuan Zhang ◽  
Andrea Marinoni ◽  
Lianru Gao ◽  
Bing Zhang
Keyword(s):  
Time Series ◽  
Image Fusion ◽  
Land Surface ◽  
Temporal Change ◽  
Window Size ◽  
Sliding Window ◽  
Spectral Variability ◽  
Landsat Images ◽  
Endmember Variability ◽  
Unmixing Model

The trade-off between spatial and temporal resolution limits the acquisition of dense time series of Landsat images, and limits the ability to properly monitor land surface dynamics in time. Spatiotemporal image fusion methods provide a cost-efficient alternative to generate dense time series of Landsat-like images for applications that require both high spatial and temporal resolution images. The Spatial and Temporal Reflectance Unmixing Model (STRUM) is a kind of spatial-unmixing-based spatiotemporal image fusion method. The temporal change image derived by STRUM lacks spectral variability and spatial details. This study proposed an improved STRUM (ISTRUM) architecture to tackle the problem by taking spatial heterogeneity of land surface into consideration and integrating the spectral mixture analysis of Landsat images. Sensor difference and applicability with multiple Landsat and coarse-resolution image pairs (L-C pairs) are also considered in ISTRUM. Experimental results indicate the image derived by ISTRUM contains more spectral variability and spatial details when compared with the one derived by STRUM, and the accuracy of fused Landsat-like image is improved. Endmember variability and sliding-window size are factors that influence the accuracy of ISTRUM. The factors were assessed by setting them to different values. Results indicate ISTRUM is robust to endmember variability and the publicly published endmembers (Global SVD) for Landsat images could be applied. Only sliding-window size has strong influence on the accuracy of ISTRUM. In addition, ISTRUM was compared with the Spatial Temporal Data Fusion Approach (STDFA), the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), the Hybrid Color Mapping (HCM) and the Flexible Spatiotemporal DAta Fusion (FSDAF) methods. ISTRUM is superior to STDFA, slightly superior to HCM in cases when the temporal change is significant, comparable with ESTARFM and a little inferior to FSDAF. However, the computational efficiency of ISTRUM is much higher than ESTARFM and FSDAF. ISTRUM can to synthesize Landsat-like images on a global scale.

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