Spatio-temporal changes of underground coal fires during 2008–2016 in Khanh Hoa coal field (North-east of Viet Nam) using Landsat time-series data

2018 ◽  
Vol 15 (12) ◽  
pp. 2703-2720 ◽  
Author(s):  
Tuyen Danh Vu ◽  
Thanh Tien Nguyen
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Temporal Changes ◽  
Series Data ◽  
Viet Nam ◽  
Coal Field ◽  
North East ◽  
Coal Fires ◽  
Spatio Temporal ◽  
Underground Coal Fires

scholarly journals Uncertainty Analysis of Object-Based Land-Cover Classification Using Sentinel-2 Time-Series Data

2020 ◽  
Vol 12 (22) ◽  
pp. 3798
Author(s):  
Lei Ma ◽  
Michael Schmitt ◽  
Xiaoxiang Zhu
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Time Series Data ◽  
Land Cover Classification ◽  
Series Data ◽  
Spectral Bands ◽  
Object Based ◽  
Spatio Temporal ◽  
Optical Satellite Images ◽  
The Impact

Recently, time-series from optical satellite data have been frequently used in object-based land-cover classification. This poses a significant challenge to object-based image analysis (OBIA) owing to the presence of complex spatio-temporal information in the time-series data. This study evaluates object-based land-cover classification in the northern suburbs of Munich using time-series from optical Sentinel data. Using a random forest classifier as the backbone, experiments were designed to analyze the impact of the segmentation scale, features (including spectral and temporal features), categories, frequency, and acquisition timing of optical satellite images. Based on our analyses, the following findings are reported: (1) Optical Sentinel images acquired over four seasons can make a significant contribution to the classification of agricultural areas, even though this contribution varies between spectral bands for the same period. (2) The use of time-series data alleviates the issue of identifying the “optimal” segmentation scale. The finding of this study can provide a more comprehensive understanding of the effects of classification uncertainty on object-based dense multi-temporal image classification.

scholarly journals Spatio-temporal assessment of ecological disturbance and its intensity in the Mangrove forest using MODIS derived disturbance index

2014 ◽  
Vol XL-8 ◽  
pp. 555-559 ◽  
Author(s):  
D. Dutta ◽  
P. K. Das ◽  
S. Paul ◽  
J. R. Sharma ◽  
V. K. Dadhwal
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Mangrove Ecosystem ◽  
Series Data ◽  
Landsat 8 ◽  
Mangrove Forests ◽  
Ecological Disturbance ◽  
Disturbance Index ◽  
Disturbed Areas ◽  
Spatio Temporal

The mangrove ecosystem of Sundarbans region plays an important ecological and socio-economical role in both India and Bangladesh. The ecological disturbance in the coastal mangrove forests are mainly attributed to the periodic cyclones caused by deep depression formed over the Bay of Bengal. In the present study, three of the major cyclones in the Sundarbans region were analyzed to establish the cause-and-effect relationship between cyclones and the resultant ecological disturbance. The Moderate Resolution Imaging Spectroradiometer (MODIS) time-series data was used to generate MODIS global disturbance index (MGDI) and its potential was explored to assess the instantaneous ecological disturbance caused by cyclones with varying landfall intensities and at different stages of mangrove phenology. The time-series MGDI was converted into the percentage change in MGDI using its multi-year mean for each pixel, and its response towards several cyclonic events was studied. The affected areas were identified by analyzing the Landsat-8 satellite data before and after the cyclone and the MGDI values of the affected areas were utilized to develop the threshold for delineation of the disturbed pixels. The selected threshold was applied on the time-series MGDI images to delineate the disturbed areas for each year individually to identify the frequently disturbed areas. The classified intensity map could able to detect the chronically affected areas, which can serve as a valuable input towards modelling the biomigration of the invasive species and efficient forest management.

scholarly journals The Plankton Lifeform Extraction Tool: A digital tool to increase the discoverability and usability of plankton time-series data

2021 ◽  
Author(s):  
Clare Ostle ◽  
Kevin Paxman ◽  
Carolyn A. Graves ◽  
Mathew Arnold ◽  
Felipe Artigas ◽  
...  
Keyword(s):  
Time Series ◽  
Food Web ◽  
Large Scale ◽  
Time Series Data ◽  
Multiple Stressors ◽  
Marine Species ◽  
Series Data ◽  
Biological Traits ◽  
Digital Tool ◽  
North East

Abstract. Plankton form the base of the marine food web and are sensitive indicators of environmental change. Plankton time-seriesare therefore an essential part of monitoring progress towards global biodiversity goals, such as the Convention onBiological Diversity Aichi Targets, and for informing ecosystem-based policy, such as the EU Marine Strategy FrameworkDirective. Multiple plankton monitoring programmes exist in Europe, but differences in sampling and analysis methodsprevent the integration of their data, constraining their utility over large spatio-temporal scales. The Plankton LifeformExtraction Tool brings together disparate European plankton datasets into a central database from which it extractsabundance time-series of plankton functional groups, called ‘lifeforms’, according to shared biological traits. This tool hasbeen designed to make complex plankton datasets accessible and meaningful for policy, public interest, and scientificdiscovery. It allows examination of large-scale shifts in lifeform abundance or distribution (for example, holoplankton beingpartially replaced by meroplankton), providing clues to how the marine environment is changing. The lifeform methodenables datasets with different plankton sampling and taxonomic analysis methodologies to be used together to provideinsights into the response to multiple stressors and robust policy evidence for decision making. Lifeform time-seriesgenerated with the Plankton Lifeform Extraction Tool currently inform plankton and food web indicators for the UK’sMarine Strategy, the EU’s Marine Strategy Framework Directive, and for the Convention for the Protection of the MarineEnvironment of the North- East Atlantic (OSPAR) biodiversity assessments. The Plankton Lifeform Extraction Toolcurrently integrates 155,000 samples, containing over 44 million plankton records, from 9 different plankton datasets withinUK and European Seas, collected between 1924 and 2017. Additional datasets can be added, and time-series updated. ThePlankton Lifeform Extraction Tool is hosted by The Archive for Marine Species and Habitats Data (DASSH) athttps://www.dassh.ac.uk/lifeforms/. The lifeform outputs are linked to specific, doi-ed, versions of the Plankton LifeformTraits Master List and each underlying dataset.

scholarly journals Missing Value Imputation of Time-Series Air-Quality Data via Deep Neural Networks

2021 ◽  
Vol 18 (22) ◽  
pp. 12213
Author(s):  
Taesung Kim ◽  
Jinhee Kim ◽  
Wonho Yang ◽  
Hunjoo Lee ◽  
Jaegul Choo
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Air Quality ◽  
Time Series Data ◽  
Quality Data ◽  
Series Data ◽  
Missing Value ◽  
Missing Value Imputation ◽  
Spatio Temporal ◽  
Air Quality Data

To prevent severe air pollution, it is important to analyze time-series air quality data, but this is often challenging as the time-series data is usually partially missing, especially when it is collected from multiple locations simultaneously. To solve this problem, various deep-learning-based missing value imputation models have been proposed. However, often they are barely interpretable, which makes it difficult to analyze the imputed data. Thus, we propose a novel deep learning-based imputation model that achieves high interpretability as well as shows great performance in missing value imputation for spatio-temporal data. We verify the effectiveness of our method through quantitative and qualitative results on a publicly available air-quality dataset.

scholarly journals Crop Yield Prediction Using Multitemporal UAV Data and Spatio-Temporal Deep Learning Models

2020 ◽  
Vol 12 (23) ◽  
pp. 4000
Author(s):  
Petteri Nevavuori ◽  
Nathaniel Narra ◽  
Petri Linna ◽  
Tarmo Lipping
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Crop Yield ◽  
Time Series Data ◽  
Weather Data ◽  
Series Data ◽  
Percentage Error ◽  
Time Series Modelling ◽  
Spatio Temporal ◽  
3D Cnn

Unmanned aerial vehicle (UAV) based remote sensing is gaining momentum worldwide in a variety of agricultural and environmental monitoring and modelling applications. At the same time, the increasing availability of yield monitoring devices in harvesters enables input-target mapping of in-season RGB and crop yield data in a resolution otherwise unattainable by openly availabe satellite sensor systems. Using time series UAV RGB and weather data collected from nine crop fields in Pori, Finland, we evaluated the feasibility of spatio-temporal deep learning architectures in crop yield time series modelling and prediction with RGB time series data. Using Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM) networks as spatial and temporal base architectures, we developed and trained CNN-LSTM, convolutional LSTM and 3D-CNN architectures with full 15 week image frame sequences from the whole growing season of 2018. The best performing architecture, the 3D-CNN, was then evaluated with several shorter frame sequence configurations from the beginning of the season. With 3D-CNN, we were able to achieve 218.9 kg/ha mean absolute error (MAE) and 5.51% mean absolute percentage error (MAPE) performance with full length sequences. The best shorter length sequence performance with the same model was 292.8 kg/ha MAE and 7.17% MAPE with four weekly frames from the beginning of the season.

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