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.

scholarly journals Mapping and Monitoring Rice Agriculture with Multisensor Temporal Mixture Models

2019 ◽  
Vol 11 (2) ◽  
pp. 181 ◽  
Author(s):  
Daniel Sousa ◽  
Christopher Small
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Mixture Models ◽  
Upper Bound ◽  
Land Surface ◽  
Atmospheric Correction ◽  
Spatial Extent ◽  
Landsat Images ◽  
Rice Agriculture ◽  
Field Station

Rice is the staple food for more than half of humanity. Accurate prediction of rice harvests is therefore of considerable global importance for food security and economic stability, especially in the developing world. Landsat sensors have collected coincident thermal and optical images for the past 35+ years, and so can provide both retrospective and near-realtime constraints on the spatial extent of rice planting and the timing of rice phenology. Thermal and optical imaging capture different physical processes, and so provide different types of information for phenologic mapping. Most analyses use only one or the other data source, omitting potentially useful information. We present a novel approach to the mapping and monitoring of rice agriculture which leverages both optical and thermal measurements. The approach relies on Temporal Mixture Models (TMMs) derived from parallel Empirical Orthogonal Function (EOF) analyses of Landsat image time series. Analysis of each image time series is performed in two stages: (1) spatiotemporal characterization, and (2) temporal mixture modeling. Characterization evaluates the covariance structure of the data, culminating in the selection of temporal endmembers (EMs) representing the most distinct phenological cycles of either vegetation abundance or surface temperature. Modeling uses these EMs as the basis for linear TMMs which map the spatial distribution of each EM phenological pattern across study area. The two metrics we analyze in parallel are (1) fractional vegetation abundance (Fv) derived from spectral mixture analysis (SMA) of optical reflectance, and (2) land surface temperature (LST) derived from brightness temperature (Tb). These metrics are chosen on the basis of being straightforward to compute for any (cloud-free) Landsat 4-8 image in the global archive. We demonstrate the method using a 90 × 120 km area in the Sacramento Valley of California. Satellite Tb retrievals are corrected to LST using a standardized atmospheric correction approach and pixelwise fractional emissivity estimates derived from SMA. LST and Tb time series are compared to field station data in 2016 and 2017. Uncorrected Tb is observed to agree with the upper bound of the envelope of air temperature observations to within 3 °C on average. As expected, LST estimates are 3 to 5 °C higher. Soil T, air T, Tb and LST estimates can all be represented as linear transformations of the same seasonal cycle. The 3D temporal feature spaces of Fv and LST clearly resolve 5 and 7 temporal EM phenologies, respectively, with strong clustering distinguishing rice from other vegetation. Results from parallel EOF analyses of coincident Fv and LST image time series over the 2016 and 2017 growing seasons suggest that TMMs based on single year Fv datasets can provide accurate maps of crop timing, while TMMs based on dual year LST datasets can provide comparable maps of year-to-year crop conversion. We also test a partial-year model midway through the 2018 growing season to illustrate a potential real-time monitoring application. Field validation confirms the monitoring model provides an upper bound estimate of spatial extent and relative timing of the rice crop accurate to 89%, even with an unusually sparse set of usable Landsat images.

Assessment of different indices from Landsat time series in burned area mapping

2020 ◽  
Author(s):  
Jinxiu Liu
Keyword(s):  
Time Series ◽  
Burkina Faso ◽  
Land Surface ◽  
Vegetation Index ◽  
Landsat Imagery ◽  
Burned Area ◽  
Free Access ◽  
Landsat Images ◽  
Area Index ◽  
Burned Area Mapping

<p>Fire is recognized as an important land surface disturbance, as it influences terrestrial carbon cycle, climate and biodiversity. Accurate and efficient mapping of burned area is beneficial for social and environmental applications. Remote sensing plays a key role in detecting burned areas and active fires from reginal to global scales. Due to the free access to the Landsat archive, studies using dense time series of Landsat imagery for burned area mapping are appearing and increasing. However, the performance of Landsat time series when using different indices for burned area mapping has not been assessed. In this study, the objective was to identify which indices can detect burned area better when using Landsat time series in savanna area of southern Burkina Faso. We selected Burned Area Index (BAI), Normalized Burned Ratio (NBR), Normalized Difference Vegetation Index (NDVI), Global Environmental Monitoring Index (GEMI) for comparison as they are commonly used indices for burned area detection. The algorithm was based on breakpoint identification and burned pixel detection using harmonic model fitting with different indices Landsat time series. It was tested in savanna area in southern Burkina Faso over 16 years with 281 Landsat images ranging from October 2000 to April 2016.The same reference data was used to evaluate the performance of burned area detection with different indices Landsat time series. The result demonstrated that BAI was the most accurate in burned area detection from Landsat time series, followed by NBR, GEMI and NDVI.</p>

scholarly journals Hierarchical Sparse Nonnegative Matrix Factorization for Hyperspectral Unmixing with Spectral Variability

2020 ◽  
Vol 12 (14) ◽  
pp. 2326 ◽  
Author(s):  
Tatsumi Uezato ◽  
Mathieu Fauvel ◽  
Nicolas Dobigeon
Keyword(s):  
Hyperspectral Image ◽  
Nonnegative Matrix ◽  
Hyperspectral Data ◽  
Spectral Variability ◽  
Data Set ◽  
Sparse Nonnegative Matrix Factorization ◽  
Data Driven Modeling ◽  
Endmember Variability ◽  
Conical Hull ◽  
Unmixing Model

Accounting for endmember variability is a challenging issue when unmixing hyperspectral data. This paper models the variability that is associated with each endmember as a conical hull defined by extremal pixels from the data set. These extremal pixels are considered as so-called prototypal endmember spectra that have meaningful physical interpretation. Capitalizing on this data-driven modeling, the pixels of the hyperspectral image are then described as combinations of these prototypal endmember spectra weighted by bundling coefficients and spatial abundances. The proposed unmixing model not only extracts and clusters the prototypal endmember spectra, but also estimates the abundances of each endmember. The performance of the approach is illustrated thanks to experiments conducted on simulated and real hyperspectral data and it outperforms state-of-the-art methods.

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

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 INTER-ANNUAL DIFFERENCE OF LAND SURFACE PHENOLOGY (LSP) TRANSACTION DATES USING TIME SERIES LANDSAT IMAGES

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 721-725
Author(s):  
L. Li ◽  
J. Li ◽  
H. Yang ◽  
Z. Jiang ◽  
L. Zhao
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Soil Temperature ◽  
Land Surface ◽  
Ground Vegetation ◽  
Growth Monitoring ◽  
Landsat Images ◽  
Land Surface Phenology ◽  
Vegetation Growth ◽  
Good Proxy

Abstract. Land surface phenology (LSP) is a kind of vital information for land cover classification and vegetation growth monitoring. Time series Landsat images, with the advantages of long observations and high spatial resolution, have been widely used in LSP identification. However, LSP transaction dates, such as start of season (SOS) and end of season (EOS), are highly influenced by the coarse temporal resolution. In this study, we compare the inter-annual difference of LSP SOS from 5 years interval, 10 years interval and all years interval Landsat images, and improve the SOS estimated model by considering the accumulated growing degree-days (AGDD) of soil temperature and soil moisture. Results indicate that LSP SOS can serve as a good proxy for reflecting ground vegetation phenology, especially using 5 years interval Landsat images. Soil temperature and soil moisture have certain influence on SOS estimation, and the R-squared value reached 0.9 after model adjustment. This study can provide guidance for estimating suitable inter-annual LSP transaction dates under different sceneries in the future.

scholarly journals A LANDSAT TIME-SERIES STACKS MODEL FOR DETECTION OF CROPLAND CHANGE

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 693-697
Author(s):  
J. Chen ◽  
J. Chen ◽  
J. Zhang
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Change Detection ◽  
Land Surface ◽  
Spectral Band ◽  
Main Idea ◽  
Landsat 8 ◽  
Continuous Change ◽  
Landsat Images ◽  
Detection Techniques

Global, timely, accurate and cost-effective cropland monitoring with a fine spatial resolution will dramatically improve our understanding of the effects of agriculture on greenhouse gases emissions, food safety, and human health. Time-series remote sensing imagery have been shown particularly potential to describe land cover dynamics. The traditional change detection techniques are often not capable of detecting land cover changes within time series that are severely influenced by seasonal difference, which are more likely to generate pseuso changes. Here,we introduced and tested LTSM ( Landsat time-series stacks model), an improved Continuous Change Detection and Classification (CCDC) proposed previously approach to extract spectral trajectories of land surface change using a dense Landsat time-series stacks (LTS). The method is expected to eliminate pseudo changes caused by phenology driven by seasonal patterns. The main idea of the method is that using all available Landsat 8 images within a year, LTSM consisting of two term harmonic function are estimated iteratively for each pixel in each spectral band .LTSM can defines change area by differencing the predicted and observed Landsat images. The LTSM approach was compared with change vector analysis (CVA) method. The results indicated that the LTSM method correctly detected the “true change” without overestimating the “false” one, while CVA pointed out “true change” pixels with a large number of “false changes”. The detection of change areas achieved an overall accuracy of 92.37 %, with a kappa coefficient of 0.676.

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