scholarly journals Implementación y evaluación del modelo Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS): estudio de caso en los Andes colombianos

2016 ◽  
pp. 83 ◽  
Author(s):  
G. M. Valencia ◽  
J. A. Anaya ◽  
F. J. Caro-Lopera
Keyword(s):  
Least Squares ◽  
Processing System ◽  
Temporal Patterns ◽  
Nonparametric Statistics ◽  
Temporal Variations ◽  
Surface Reflectance ◽  
Landsat Images ◽  
Robust Modeling ◽  
Ecosystem Disturbance ◽  
Colombian Andes

<p>This paper analyzes the reflectance obtained with a series of Landsat images processed with LEDAPS model in a region of the Colombian Andes. A total of 38 images of TM and ETM sensors were calibrated to surface reflectance using LEDAPS in order to determine difference among bands of the same sensor, difference between sensors and analyze temporal patterns. Exact nonparametric statistics allow to conclude that: a) surface reflectance for band 1–5 and 7 were significantly different and this difference remains among images of different dates; b) there are statistical similarities between the TM and ETM sensors bands; c) temporal variations on surface reflectance from the years 1986 to 2013 with the sensors studied are not statistically significant. These results are supported by the implementation of robust modeling with various methods resistant to unusual observations and other typical problems of the classical least squares modeling.</p>

Temporal patterns of heart rate and selected arrhythmias for 48 hours after cardiac surgery

1993 ◽  
Vol 2 (5) ◽  
pp. 359-370 ◽  
Author(s):  
SL Woods ◽  
L Felver ◽  
R Hoeksel
Keyword(s):  
Heart Rate ◽  
Cardiac Surgery ◽  
Critically Ill ◽  
Longitudinal Design ◽  
Temporal Patterns ◽  
Temporal Variations ◽  
Individual Subject ◽  
Cosinor Analysis ◽  
Premature Ventricular Complexes ◽  
Critically Ill Adults

OBJECTIVE: To describe the temporal patterns of heart rate and arrhythmias in the immediate postoperative period following cardiac surgery. Six postoperative cardiac surgical patients with a mean age of 48.3 years were studied. DESIGN: Descriptive longitudinal design. SETTING: Cardiac surgical ICU. METHODS: Heart rate and arrhythmias were recorded continuously for 48 hours from a cardiac monitor using a Holter tape recorder. Environmental and treatment data were noted throughout data collection by trained nonparticipant observers. RESULTS: Mean heart rate and incidence of arrhythmias were different between the 2 study days; therefore, data were divided into two segments (A and B). These differences coincided with extubation in most cases. Individual subject cosinor analysis revealed 24-hour rhythms of heart rate in both segments in all subjects except segment B for one subject. Rhythms of shorter periods were also found. In segment A individual subjects' acrophases (peak times of fitted curves) occurred later than expected for subjects' prehospitalization sleep-wake schedule, whereas in segment B they occurred earlier. Cosinor analysis of arrhythmias revealed significant 24-hour rhythms in both segments in one of the three subjects with premature atrial complexes, two of the four subjects with premature ventricular complexes and both subjects with ventricular couplets. Four-hour rhythms were found in premature atrial complexes (n = 1), atrial tachycardia (n = 1) and premature ventricular complexes (n = 3). Acrophases for arrhythmias varied among patients. During segment B the 4-hour-rhythm acrophases in heart rate and arrhythmias were related to the timing of respiratory therapy. CONCLUSIONS: Temporal variations in heart rate could be identified in these six critically ill adults. Rhythm parameters changed during the first 48 hours after cardiac surgery. In those who had arrhythmias, some patients demonstrated temporal patterns in the incidence of selected arrhythmias. Further study is needed to describe the temporal patterns of heart rate and arrhythmias in varied groups of critically ill persons in a variety of settings.

scholarly journals Spatial and temporal variations of the surface albedo and other factors influencing Urumqi Glacier No. 1 in Tien Shan, China

2017 ◽  
Vol 63 (241) ◽  
pp. 899-911 ◽  
Author(s):  
XIAOYING YUE ◽  
JUN ZHAO ◽  
ZHONGQIN LI ◽  
MINGJUN ZHANG ◽  
JIN FAN ◽  
...  
Keyword(s):  
Surface Albedo ◽  
Temporal Variations ◽  
Surface Energy Budget ◽  
Tien Shan ◽  
Spatial And Temporal Variations ◽  
Spatial And Temporal Variability ◽  
Landsat 8 ◽  
Topographic Effects ◽  
Landsat Images ◽  
Factors Influencing

ABSTRACTGlacier albedo controls the surface energy budget, the variability of which affects the glacier surface melt rate and, in turn, impacts the mass balance of the glacier. During 2013 and 2014, spatial and temporal variations of albedo were investigated using 18 Landsat images of Urumqi Glacier No. 1. Factors influencing these spatiotemporal profiles were analyzed. An established retrieval process, including geolocation, radiometric calibration, atmospheric, topographic, and anisotropic correction and narrow- to broadband conversion, was applied for the first time to Landsat-8 images. Differences between Landsat image derived albedo values and albedo values measured using a handheld spectroradiometer ranged from −0.024 to 0.049. Spatial and temporal variations of surface albedo were significant, especially in the ablation area. The variability of the values of ice albedo ranged from 0.06 to 0.44 due to topographic effects and light-absorbing impurities. The results suggest that this retrieval method can be used to investigate the spatial and temporal variability of surface albedo from Landsat-8 images on mountain glaciers. Moreover, as constant albedo values for ice and snow cannot be assumed, the distribution of albedo was not completely dependent on altitude under conditions of more intense ablation, and by reason of light-absorbing impurities during the melt season.

scholarly journals MODEL KOREKSI ATMOSFER CITRA LANDSAT-7 (ATMOSPHERIC CORRECTION MODELS OF LANDSAT-7 IMAGERY)

2018 ◽  
Vol 14 (2) ◽  
Author(s):  
Fadila Muchsin ◽  
Liana Fibriawati ◽  
Kuncoro Adhi Pradhono
Keyword(s):  
Atmospheric Correction ◽  
Solar Spectrum ◽  
Processing System ◽  
Line Of Sight ◽  
Adaptive Processing ◽  
Spectral Pattern ◽  
Ecosystem Disturbance ◽  
Corrected Image ◽  
Landsat 7 ◽  
Satellite Signal

Three methods of atmospheric correction, Second Simulation of the Satellite Signal in the Solar Spectrum (6S), Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) and the model Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH), have been applied to the level 1T Landsat-7 image Jakarta area. The atmospheric corrected image is then compared with the TOA reflectance image. The results show that there is an improvement of the spectral pattern on the TOA reflectance image by the decrease of the reflectance value of each object by (1 - 11) % after the atmospheric correction of all models for visible bands (blue, green and red). In the NIR and SWIR bands there is an increase in the spectral value of about 1% to the TOA reflectance on all objects except wetland for the LEDAPS model. The percentage of the increase and the decrease in spectral values of 6S and FLAASH models have the same tendency. Analyzes were also performed on the NDVI values of each model, where NDVI values were relatively higher after atmospheric correction. The NDVI value of rice crop on FLAASH model is the same as 6S model that is equal to 0.95 and for wetland, it has the same value between FLAASH model and LEDAPS which is 0.23. NDVI value of entire scene for FLAASH model = 0.63, LEDAPS model = 0.56 and 6S model = 0.66. Before the atmospheric correction, the TOA is 0.45. Abstrak Tiga metode koreksi atmosfer diantaranya  Second Simulation of the Satellite Signal in the Solar Spectrum (6S), Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) dan model Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) telah diterapkan pada citra Landsat-7 level 1T wilayah Jakarta. Citra yang telah terkoreksi atmosfer dibandingkan dengan citra reflektan TOA. Hasilnya menunjukkan bahwa terdapat perbaikan pola spektral pada citra reflektan TOA dengan adanya penurunan nilai reflektan setiap obyek sebesar (1 – 11) % setelah dilakukan koreksi atmosfer pada semua model untuk kanal-kanal visible (blue, green dan red). Pada kanal NIR dan SWIR terjadi kenaikan nilai spektral yaitu sekitar 1% terhadap reflektan TOA pada semua objek terkecuali objek lahan basah untuk model LEDAPS. Persentase kenaikan dan penurunan nilai spektral model 6S dan FLAASH memiliki kecenderungan yang sama. Analisis juga dilakukan terhadap nilai NDVI masing-masing model, dimana nilai NDVI relatif lebih tinggi setelah koreksi atmosfer. Nilai NDVI tanaman padi pada model FLAASH sama dengan model 6S yaitu sebesar 0.95 dan untuk lahan basah memiliki nilai yang sama antara model FLAASH dan LEDAPS yaitu 0.23. Nilai NDVI seluruh scene untuk model FLAASH = 0.63, model LEDAPS = 0.56 dan model 6S = 0.66. Sebelum koreksi atmosfer (TOA) adalah 0.45. 

scholarly journals Automatic Methodology to Detect the Coastline from Landsat Images with a New Water Index Assessed on Three Different Spanish Mediterranean Deltas

2019 ◽  
Vol 11 (18) ◽  
pp. 2186 ◽  
Author(s):  
Sandra Viaña-Borja ◽  
Miguel Ortega-Sánchez
Keyword(s):  
Remote Sensing ◽  
Satellite Images ◽  
Surface Reflectance ◽  
Landsat Images ◽  
Water Index ◽  
Measuring Techniques ◽  
Pixel Analysis ◽  
Land Covers ◽  
Shortwave Infrared ◽  
Bicubic Interpolation

Due to the importance of coastline detection in coastal studies, different methods have been developed in recent decades in accordance with the evolution of measuring techniques such as remote sensing. This work proposes an automatic methodology with new water indexes to detect the coastline from different multispectral Landsat images; the methodology is applied to three Spanish deltas in the Mediterranean Sea. The new water indexes use surface reflectance rather than top-of-atmosphere reflectance from blue and shortwave infrared (SWIR 2) Landsat bands. A total of 621 sets of images were analyzed from three different Landsat sensors with a moderate spatial resolution of 30 m. Our proposal, which was compared to the most commonly used water indexes, showed outstanding performance in automatic detection of the coastline in 96% of the data analyzed, which also reached the minimum value of bias of − 0.91 m and a standard deviation ranging from ±4.7 and ±7.29 m in some cases in contrast to the existing values. Bicubic interpolation was evaluated for a simple sub-pixel analysis to assess its capability in improving the accuracy of coastline extraction. Our methodology represents a step forward in automatic coastline detection that can be applied to micro-tidal coastal sites with different land covers using many multi-sensor satellite images.

scholarly journals A Robust Method for Generating High-Spatiotemporal-Resolution Surface Reflectance by Fusing MODIS and Landsat Data

2020 ◽  
Vol 12 (14) ◽  
pp. 2312
Author(s):  
Junming Yang ◽  
Yunjun Yao ◽  
Yongxia Wei ◽  
Yuhu Zhang ◽  
Kun Jia ◽  
...  
Keyword(s):  
Time Series ◽  
Data Fusion ◽  
Land Surface ◽  
Time Series Data ◽  
Temporal Variations ◽  
Series Data ◽  
Surface Reflectance ◽  
Spatiotemporal Resolution ◽  
Ecological Processes ◽  
Clustering Model

The methods for accurately fusing medium- and high-spatial-resolution satellite reflectance are vital for monitoring vegetation biomass, agricultural irrigation, ecological processes and climate change. However, the currently existing fusion methods cannot accurately capture the temporal variation in reflectance for heterogeneous landscapes. In this study, we proposed a new method, the spatial and temporal reflectance fusion method based on the unmixing theory and a fuzzy C-clustering model (FCMSTRFM), to generate Landsat-like time-series surface reflectance. Unlike other data fusion models, the FCMSTRFM improved the similarity of pixels grouped together by combining land cover maps and time-series data cluster algorithms to define endmembers. The proposed method was tested over a 2000 km2 study area in Heilongjiang Provence, China, in 2017 and 2018 using ten images. The results show that the accuracy of the FCMSTRFM is better than that of the popular enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) (correlation coefficient (R): 0.8413 vs. 0.7589; root mean square error (RMSE): 0.0267 vs. 0.0401) and the spatial-temporal data fusion approach (STDFA) (R: 0.8413 vs. 0.7666; RMSE: 0.0267 vs. 0.0307). Importantly, the FCMSTRFM was able to maintain the details of temporal variations in complicated landscapes. The proposed method provides an alternative method to monitor the dynamics of land surface variables over complicated heterogeneous regions.

Spatial/Temporal Variations of Crime: A Routine Activity Theory Perspective

2017 ◽  
Vol 62 (7) ◽  
pp. 1967-1991 ◽  
Author(s):  
Silas Nogueira de Melo ◽  
Débora V. S. Pereira ◽  
Martin A. Andresen ◽  
Lindon Fonseca Matias
Keyword(s):  
Activity Theory ◽  
Spatial Patterns ◽  
Latin American ◽  
Temporal Patterns ◽  
Temporal Variations ◽  
Routine Activity Theory ◽  
Routine Activity ◽  
Temporal And Spatial ◽  
Crime Types ◽  
Brazilian Culture

Temporal and spatial patterns of crime in Campinas, Brazil, are analyzed considering the relevance of routine activity theory in a Latin American context. We use geo-referenced criminal event data, 2010-2013, analyzing spatial patterns using census tracts and temporal patterns considering seasons, months, days, and hours. Our analyses include difference in means tests, count-based regression models, and Kulldorff’s scan test. We find that crime in Campinas, Brazil, exhibits both temporal and spatial-temporal patterns. However, the presence of these patterns at the different temporal scales varies by crime type. Specifically, not all crime types have statistically significant temporal patterns at all scales of analysis. As such, routine activity theory works well to explain temporal and spatial-temporal patterns of crime in Campinas, Brazil. However, local knowledge of Brazilian culture is necessary for understanding a portion of these crime patterns.

scholarly journals Discovering Temporal Patterns in Longitudinal Nontargeted Metabolomics Data via Group and Nuclear Norm Regularized Multivariate Regression

Metabolites ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 33 ◽  
Author(s):  
Zhaozhou Lin ◽  
Qiao Zhang ◽  
Shengyun Dai ◽  
Xiaoyan Gao
Keyword(s):  
Discriminant Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Temporal Patterns ◽  
Nuclear Norm ◽  
Compact Set ◽  
Metabolomics Data ◽  
Pattern Recognition Methods ◽  
High Prediction ◽  
Consecutive Time

Temporal associations in longitudinal nontargeted metabolomics data are generally ignored by common pattern recognition methods such as partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA). To discover temporal patterns in longitudinal metabolomics, a multitask learning (MTL) method employing structural regularization was proposed. The group regularization term of the proposed MTL method enables the selection of a small number of tentative biomarkers while maintaining high prediction accuracy. Meanwhile, the nuclear norm imposed into the regression coefficient accounts for the interrelationship of the metabolomics data obtained on consecutive time points. The effectiveness of the proposed method was demonstrated by comparison study performed on a metabolomics dataset and a simulating dataset. The results showed that a compact set of tentative biomarkers charactering the whole antipyretic process of Qingkailing injection were selected with the proposed method. In addition, the nuclear norm introduced in the new method could help the group norm to improve the method’s recovery ability.

scholarly journals Temporal Patterns in Illumination Conditions and Its Effect on Vegetation Indices Using Landsat on Google Earth Engine

2020 ◽  
Vol 12 (2) ◽  
pp. 211 ◽  
Author(s):  
Pablo Martín-Ortega ◽  
Luis Gonzaga García-Montero ◽  
Nicole Sibelet
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Temporal Patterns ◽  
Google Earth ◽  
Surface Reflectance ◽  
Enhanced Vegetation Index ◽  
Google Earth Engine ◽  
Level 1 ◽  
Irregular Topography

Vegetation indices (VI) describe vegetation structure and functioning but they are affected by illumination conditions (IC). Moreover, the fact that the effect of the IC on VI can be stronger than other biophysical or seasonal processes is under debate. Using Google Earth Engine and the latest Landsat Surface Reflectance level 1 data, we evaluated the temporal patterns of IC and two VI, the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) in a mountainous tropical forest during the years 1984–2017. We evaluated IC and VI at different times, their relationship with the topography and the correlations between them. We show that IC is useful for understanding the patterns of variation between VI and IC at the pixel level using Landsat sensors. Our findings confirmed a strong correlation between EVI and IC and less between NDVI and IC. We found a significant increase in IC, EVI, and NDVI throughout time due to an improvement in the position of all Landsat sensors. Our results reinforce the need to consider IC to interpret VI over long periods using Landsat data in order to increase the precision of monitoring VI in irregular topography.

scholarly journals Evaluating the spatial and temporal variations of aquatic weeds (Biomass) on Lower Volta River using multi-sensor Landsat Images and machine learning

Heliyon ◽  
2021 ◽  
Vol 7 (5) ◽  
pp. e07080
Author(s):  
Clement Nyamekye ◽  
Samuel Anim Ofosu ◽  
Richard Arthur ◽  
Gabriel Osei ◽  
Linda Boamah Appiah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Temporal Variations ◽  
Spatial And Temporal Variations ◽  
Aquatic Weeds ◽  
Landsat Images
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