scholarly journals Short-Term Variability in Alaska Ice-Marginal Lake Area: Implications for Long-Term Studies

2021 ◽  
Vol 13 (19) ◽  
pp. 3955
Author(s):  
Anton M. Hengst ◽  
William Armstrong ◽  
Brianna Rick ◽  
Daniel McGrath
Keyword(s):  
Vegetation Index ◽  
Google Earth ◽  
Landsat 8 ◽  
Lake Area ◽  
Dynamic Features ◽  
Short Term ◽  
South Central ◽  
Short Term Variability ◽  
Long Term Studies

Lakes in direct contact with glaciers (ice-marginal lakes) are found across alpine and polar landscapes. Many studies characterize ice-marginal lake behavior over multi-decadal timescales using either episodic ~annual images or multi-year mosaics. However, ice-marginal lakes are dynamic features that experience short-term (i.e., day to year) variations in area and volume superimposed on longer-term trends. Through aliasing, this short-term variability could result in erroneous long-term estimates of lake change. We develop and implement an automated workflow in Google Earth Engine to quantify monthly behavior of ice-marginal lakes between 2013 and 2019 across south-central Alaska using Landsat 8 imagery. We employ a supervised Mahalanobis minimum-distance land cover classifier incorporating three datasets found to maximize classifier performance: shortwave infrared imagery, the normalized difference vegetation index (NDVI), and spatially filtered panchromatic reflectance. We observe physically-meaningful ice-marginal lake area variance on sub-annual timescales, with the median area fluctuation of an ice-marginal lake found to be 10.8% of its average area. The median signal (slow lake growth) to noise (physically-meaningful short-term area variability) ratio is 1.5:1, indicating that short-term variability is responsible for ~33% of observed area change in the median ice-marginal lake. The magnitude of short-term area variability is similar for ice-marginal and nonglacial lakes, suggesting that the cause of observed variations is not of glacial origin. These data provide a new context for interpreting behaviors observed in multi-decadal studies and encourage attention to sub-annual behavior of ice-marginal lakes even in long-term studies.

scholarly journals Consistent Long-Term Monthly Coastal Wetland Vegetation Monitoring Using a Virtual Satellite Constellation

2021 ◽  
Vol 13 (3) ◽  
pp. 438
Author(s):  
Subrina Tahsin ◽  
Stephen C. Medeiros ◽  
Arvind Singh
Keyword(s):  
Vegetation Index ◽  
Coastal Wetland ◽  
Thermal Emission ◽  
Mean Squared Error ◽  
Wetland Vegetation ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
Vegetation Monitoring ◽  
Time Space

Long-term monthly coastal wetland vegetation monitoring is the key to quantifying the effects of natural and anthropogenic events, such as severe storms, as well as assessing restoration efforts. Remote sensing data products such as Normalized Difference Vegetation Index (NDVI), alongside emerging data analysis techniques, have enabled broader investigations into their dynamics at monthly to decadal time scales. However, NDVI data suffer from cloud contamination making periods within the time series sparse and often unusable during meteorologically active seasons. This paper proposes a virtual constellation for NDVI consisting of the red and near-infrared bands of Landsat 8 Operational Land Imager, Sentinel-2A Multi-Spectral Instrument, and Advanced Spaceborne Thermal Emission and Reflection Radiometer. The virtual constellation uses time-space-spectrum relationships from 2014 to 2018 and a random forest to produce synthetic NDVI imagery rectified to Landsat 8 format. Over the sample coverage area near Apalachicola, Florida, USA, the synthetic NDVI showed good visual coherence with observed Landsat 8 NDVI. Comparisons between the synthetic and observed NDVI showed Root Mean Squared Error and Coefficient of Determination (R2) values of 0.0020 sr−1 and 0.88, respectively. The results suggest that the virtual constellation was able to mitigate NDVI data loss due to clouds and may have the potential to do the same for other data. The ability to participate in a virtual constellation for a useful end product such as NDVI adds value to existing satellite missions and provides economic justification for future projects.

scholarly journals Vegetation Types Mapping Using Multi-Temporal Landsat Images in the Google Earth Engine Platform

2021 ◽  
Vol 13 (22) ◽  
pp. 4683
Author(s):  
Masoumeh Aghababaei ◽  
Ataollah Ebrahimi ◽  
Ali Asghar Naghipour ◽  
Esmaeil Asadi ◽  
Jochem Verrelst
Keyword(s):  
Vegetation Index ◽  
Google Earth ◽  
Optimal Time ◽  
Quantitative Detection ◽  
Landsat 8 ◽  
Vegetation Types ◽  
Landsat Images ◽  
Multi Temporal ◽  
Google Earth Engine ◽  
Land Covers

Vegetation Types (VTs) are important managerial units, and their identification serves as essential tools for the conservation of land covers. Despite a long history of Earth observation applications to assess and monitor land covers, the quantitative detection of sparse VTs remains problematic, especially in arid and semiarid areas. This research aimed to identify appropriate multi-temporal datasets to improve the accuracy of VTs classification in a heterogeneous landscape in Central Zagros, Iran. To do so, first the Normalized Difference Vegetation Index (NDVI) temporal profile of each VT was identified in the study area for the period of 2018, 2019, and 2020. This data revealed strong seasonal phenological patterns and key periods of VTs separation. It led us to select the optimal time series images to be used in the VTs classification. We then compared single-date and multi-temporal datasets of Landsat 8 images within the Google Earth Engine (GEE) platform as the input to the Random Forest classifier for VTs detection. The single-date classification gave a median Overall Kappa (OK) and Overall Accuracy (OA) of 51% and 64%, respectively. Instead, using multi-temporal images led to an overall kappa accuracy of 74% and an overall accuracy of 81%. Thus, the exploitation of multi-temporal datasets favored accurate VTs classification. In addition, the presented results underline that available open access cloud-computing platforms such as the GEE facilitates identifying optimal periods and multitemporal imagery for VTs classification.

Assessment of the State of the Territories of Closed Waste Disposal Facilities by Vegetation Indices

2021 ◽  
Vol 25 (9) ◽  
pp. 30-37
Author(s):  
N.N. Sliusar ◽  
A.P. Belousova ◽  
G.M. Batrakova ◽  
R.D. Garifzyanov ◽  
M. Huber-Humer ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Waste Disposal ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Cost Effective ◽  
Google Earth ◽  
The State ◽  
Landsat 8 ◽  
The Earth

The possibilities of using remote sensing of the Earth data to assess the formation of phytocenoses at reclaimed dumps and landfills are presented. The objects of study are landfills and dumps in the Perm Territory, which differed from each other in the types and timing of reclamation work. The state of the vegetation cover on the reclaimed and self-overgrowing objects was compared with the reference plots with naturally formed herbage of zonal meadow vegetation. The process of reclamation of the territory of closed landfills was assessed by the presence and homogeneity of the vegetation layer and by the values of the vegetation index NDVI. To identify the dynamics of changes in the vegetation cover, we used multi-temporal satellite images from the open resources of Google Earth and images in the visible and infrared ranges of the Landsat-5/TM and Landsat-8/OLI satellites. It is shown that the data of remote sensing of the Earth, in particular the analysis of vegetation indices, can be used to assess the dynamics of overgrowing of territories of reclaimed waste disposal facilities, as well as an additional and cost-effective method for monitoring the restoration of previously disturbed territories.

scholarly journals Mapping of Cotton Fields Within-Season Using Phenology-Based Metrics Derived from a Time Series of Landsat Imagery

2020 ◽  
Vol 12 (18) ◽  
pp. 3038
Author(s):  
Dhahi Al-Shammari ◽  
Ignacio Fuentes ◽  
Brett M. Whelan ◽  
Patrick Filippi ◽  
Thomas F. A. Bishop
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Landsat Imagery ◽  
Google Earth ◽  
Growth Stages ◽  
Landsat 8 ◽  
Crop Type ◽  
Eastern Australia ◽  
Normalised Difference Vegetation Index ◽  
Cotton Fields

A phenology-based crop type mapping approach was carried out to map cotton fields throughout the cotton-growing areas of eastern Australia. The workflow was implemented in the Google Earth Engine (GEE) platform, as it is time efficient and does not require processing in multiple platforms to complete the classification steps. A time series of Normalised Difference Vegetation Index (NDVI) imagery were generated from Landsat 8 Surface Reflectance Tier 1 (L8SR) and processed using Fourier transformation. This was used to produce the harmonised-NDVI (H-NDVI) from the original NDVI, and then phase and amplitude values were generated from the H-NDVI to visualise active cotton in the targeted fields. Random Forest (RF) models were built to classify cotton at early, mid and late growth stages to assess the ability of the model to classify cotton as the season progresses, with phase, amplitude and other individual bands as predictors. Results obtained from leave-one-season-out cross validation (LOSOCV) indicated that Overall Accuracy (OA), Kappa, Producer’s Accuracies (PA) and User’s Accuracy (UA), increased significantly when adding amplitude and phase as predictor variables to the model, than prediction using H-NDVI or raw bands only. Commission and omission errors were reduced significantly as the season progressed and more in-season imagery was available. The methodology proposed in this study can map cotton crops accurately based on the reconstruction of the unique cotton reflectance trajectory through time. This study confirms the importance of phenological metrics in improving in-season cotton fields mapping across eastern Australia. This model can be used in conjunction with other datasets to forecast yield based on the mapped crop type for improved decision making related to supply chain logistics and seasonal outlooks for production.

scholarly journals Determination of the Normalized Difference Vegetation Index (NDVI) with Top-of-Canopy (TOC) Reflectance from a KOMPSAT-3A Image Using Orfeo ToolBox (OTB) Extension

2020 ◽  
Vol 9 (4) ◽  
pp. 257 ◽  
Author(s):  
Kiwon Lee ◽  
Kwangseob Kim ◽  
Sun-Gu Lee ◽  
Yongseung Kim
Keyword(s):  
Open Source ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spectral Response ◽  
Atmospheric Correction ◽  
Urban Forestry ◽  
Google Earth ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Reflectance Data

Surface reflectance data obtained by the absolute atmospheric correction of satellite images are useful for land use applications. For Landsat and Sentinel-2 images, many radiometric processing methods exist, and the images are supported by most types of commercial and open-source software. However, multispectral KOMPSAT-3A images with a resolution of 2.2 m are currently lacking tools or open-source resources for obtaining top-of-canopy (TOC) reflectance data. In this study, an atmospheric correction module for KOMPSAT-3A images was newly implemented into the optical calibration algorithm in the Orfeo Toolbox (OTB), with a sensor model and spectral response data for KOMPSAT-3A. Using this module, named OTB extension for KOMPSAT-3A, experiments on the normalized difference vegetation index (NDVI) were conducted based on TOC reflectance data with or without aerosol properties from AERONET. The NDVI results for these atmospherically corrected data were compared with those from the dark object subtraction (DOS) scheme, a relative atmospheric correction method. The NDVI results obtained using TOC reflectance with or without the AERONET data were considerably different from the results obtained from the DOS scheme and the Landsat-8 surface reflectance of the Google Earth Engine (GEE). It was found that the utilization of the aerosol parameter of the AERONET data affects the NDVI results for KOMPSAT-3A images. The TOC reflectance of high-resolution satellite imagery ensures further precise analysis and the detailed interpretation of urban forestry or complex vegetation features.

scholarly journals Creatine Use in Sports

2017 ◽  
Vol 10 (1) ◽  
pp. 31-34 ◽  
Author(s):  
Jessica Butts ◽  
Bret Jacobs ◽  
Matthew Silvis
Keyword(s):  
Dietary Supplement ◽  
National Collegiate Athletic Association ◽  
Creatine Supplementation ◽  
Sports Performance ◽  
Short Term ◽  
Level Of Evidence ◽  
Improved Performance ◽  
Google Search ◽  
Long Term Studies

Context: The use of creatine as a dietary supplement has become increasingly popular over the past several decades. Despite the popularity of creatine, questions remain with regard to dosing, effects on sports performance, and safety. Evidence Acquisition: PubMed was searched for articles published between 1980 and January 2017 using the terms creatine, creatine supplementation, sports performance, and dietary supplements. An additional Google search was performed to capture National Collegiate Athletic Association–specific creatine usage data and US dietary supplement and creatine sales. Study Design: Clinical review. Level of Evidence: Level 4. Results: Short-term use of creatine is considered safe and without significant adverse effects, although caution should be advised as the number of long-term studies is limited. Suggested dosing is variable, with many different regimens showing benefits. The safety of creatine supplementation has not been studied in children and adolescents. Currently, the scientific literature best supports creatine supplementation for increased performance in short-duration, maximal-intensity resistance training. Conclusion: While creatine appears to be safe and effective for particular settings, whether creatine supplementation leads to improved performance on the field of play remains unknown.

scholarly journals Combination of Landsat 8 OLI and Sentinel-1 SAR Time-Series Data for Mapping Paddy Fields in Parts of West and Central Java Provinces, Indonesia

2020 ◽  
Vol 9 (11) ◽  
pp. 663
Author(s):  
Sanjiwana Arjasakusuma ◽  
Sandiaga Swahyu Kusuma ◽  
Raihan Rafif ◽  
Siti Saringatin ◽  
Pramaditya Wicaksono
Keyword(s):  
Spatial Data ◽  
Vegetation Index ◽  
Google Earth ◽  
Paddy Fields ◽  
Series Data ◽  
Landsat 8 ◽  
Spectral Indices ◽  
Seamless Integration ◽  
Synthetic Aperture Radar Data ◽  
Central Java

The rise of Google Earth Engine, a cloud computing platform for spatial data, has unlocked seamless integration for multi-sensor and multi-temporal analysis, which is useful for the identification of land-cover classes based on their temporal characteristics. Our study aims to employ temporal patterns from monthly-median Sentinel-1 (S1) C-band synthetic aperture radar data and cloud-filled monthly spectral indices, i.e., Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), and Normalized Difference Built-up Index (NDBI), from Landsat 8 (L8) OLI for mapping rice cropland areas in the northern part of Central Java Province, Indonesia. The harmonic function was used to fill the cloud and cloud-masked values in the spectral indices from Landsat 8 data, and smile Random Forests (RF) and Classification And Regression Trees (CART) algorithms were used to map rice cropland areas using a combination of monthly S1 and monthly harmonic L8 spectral indices. An additional terrain variable, Terrain Roughness Index (TRI) from the SRTM dataset, was also included in the analysis. Our results demonstrated that RF models with 50 (RF50) and 80 (RF80) trees yielded better accuracy for mapping the extent of paddy fields, with user accuracies of 85.65% (RF50) and 85.75% (RF80), and producer accuracies of 91.63% (RF80) and 93.48% (RF50) (overall accuracies of 92.10% (RF80) and 92.47% (RF50)), respectively, while CART yielded a user accuracy of only 84.83% and a producer accuracy of 80.86%. The model variable importance in both RF50 and RF80 models showed that vertical transmit and horizontal receive (VH) polarization and harmonic-fitted NDVI were identified as the top five important variables, and the variables representing February, April, June, and December contributed more to the RF model. The detection of VH and NDVI as the top variables which contributed up to 51% of the Random Forest model indicated the importance of the multi-sensor combination for the identification of paddy fields.

scholarly journals Fetal Vibroacoustic Stimulation in Computerized Cardiotocographic Analysis: The Role of Short-Term Variability and Approximate Entropy

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Maria Laura Annunziata ◽  
Mariamaddalena Scala ◽  
Natascia Giuliano ◽  
Salvatore Tagliaferri ◽  
Olga Carmela Maria Imperato ◽  
...  
Keyword(s):  
High Risk ◽  
Perinatal Outcomes ◽  
Approximate Entropy ◽  
Computer Assisted ◽  
Short Term ◽  
Fetal Movements ◽  
The Impact ◽  
Short Term Variability

The aim of this study was to evaluate the impact of vibroacoustic stimulation (VAS) on computerized cardiotocography short-term variability (STV) and approximate entropy (ApEn) in both low- and high-risk pregnancies. VAS was performed on 121 high- and 95 low-risk pregnancies after 10 minutes of continuous quiet, while their FHR parameters were monitored and recorded by cCTG analysis. Fetal heart rate was recorded using a computer-assisted equipment. Baseline FHR, accelerations, decelerations, STV, long-term irregularity (LTI), ApEn, and fetal movements (FMs) were calculated for defined observational periods before VAS and after 10 minutes. Data were also investigated in relationship with the perinatal outcome. In each group of patients, FHR after VAS remained almost unmodified. Fetal movements significantly increased after VAS in both groups. Results show that only in the high-risk pregnancies, the increase of STV and the decrease of ApEn after VAS were significantly associated with favorable perinatal outcomes.

The influence of mixtures of Stylosanthes spp. accessions on the occurrence of anthracnose caused by Colletotrichum gloeosporioides

1994 ◽  
Vol 45 (1) ◽  
pp. 203 ◽  
Author(s):  
RD Davis ◽  
S Chakraborty ◽  
DF Cameron ◽  
JAG Irwin ◽  
RM Boland
Keyword(s):  
Colletotrichum Gloeosporioides ◽  
Resistant Cultivar ◽  
Pure Stand ◽  
Northern Australia ◽  
Short Term ◽  
Mixed Stands ◽  
The Mean ◽  
Moderately Resistant ◽  
Long Term Studies

The effectiveness of using accession mixtures of Stylosanthes spp. to manage anthracnose (Colletotrichum gloeosporioides) in pastures in northern Australia was examined during three consecutive years. Two mixtures containing six accessions were compared with the components grown as pure stands. No significant differences in anthracnose incidence (proportion of infected plants/plot) were indicated between the two mixtures and the mean incidence of their respective components grown in pure swards. Areas under the disease progress curves for the accessions were not significantly different between pure and mixed stands of the cultivars other than Seca and Verano. Resistant cultivar Seca developed more disease in a mixture than in a pure stand, and moderately resistant Verano had less disease in a mixture than in a pure stand. In the short term, no apparent anthracnose control advantage is achieved in highly susceptible accessions of Stylosanthes spp. when they are included in mixtures with less susceptible accessions. Long term studies involving grazing animals are necessary to adequately evaluate control of this disease through the use of mixtures.

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