scholarly journals High-Frequency Glacial Lake Mapping Using Time Series of Sentinel-1A/1B SAR Imagery: An Assessment for the Southeastern Tibetan Plateau

2020 ◽  
Vol 17 (3) ◽  
pp. 1072 ◽  
Author(s):  
Meimei Zhang ◽  
Fang Chen ◽  
Bangsen Tian ◽  
Dong Liang ◽  
Aqiang Yang
Keyword(s):  
Time Series ◽  
Tibetan Plateau ◽  
High Frequency ◽  
Glacial Lake ◽  
Time Range ◽  
Support Vector ◽  
Time Interval ◽  
Full Time ◽  
Glacial Lakes ◽  
Southeastern Tibetan Plateau

Glacial lakes are an important component of the cryosphere in the Tibetan Plateau. In response to climate warming, they threaten the downstream lives, ecological environment, and public infrastructures through outburst floods within a short time. Although most of the efforts have been made toward extracting glacial lake outlines and detect their changes with remotely sensed images, the temporal frequency and spatial resolution of glacial lake datasets are generally not fine enough to reflect the detailed processes of glacial lake dynamics, especially for potentially dangerous glacial lakes with high-frequency variability. By using full time-series Sentinel-1A/1B imagery over a year, this study presents a new systematic method to extract the glacial lake outlines that have a fast variability in the southeastern Tibetan Plateau with a time interval of six days. Our approach was based on a level-set segmentation, combined with a median pixel composition of synthetic aperture radar (SAR) backscattering coefficients stacked as a regularization term, to robustly estimate the lake extent across the observed time range. The mapping results were validated against manually digitized lake outlines derived from Gaofen-2 panchromatic multi-spectral (GF-2 PMS) imagery, with an overall accuracy and kappa coefficient of 96.54% and 0.95, respectively. In comparison with results from classical supervised support vector machine (SVM) and unsupervised Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) methods, the proposed method proved to be much more robust and effective at detecting glacial lakes with irregular boundaries that have similar backscattering as the surroundings. This study also demonstrated the feasibility of time-series Sentinel-1A/1B SAR data in the continuous monitoring of glacial lake outline dynamics.

scholarly journals High-frequency glacial lake mapping using time series of Sentinel-1A/1B SAR imagery: An assessment for southeastern Tibetan Plateau

2019 ◽  
Author(s):  
Meimei Zhang ◽  
Fang Chen ◽  
Bangsen Tian ◽  
Dong Liang ◽  
Aqiang Yang
Keyword(s):  
Time Series ◽  
Tibetan Plateau ◽  
High Frequency ◽  
Glacial Lake ◽  
Time Range ◽  
Time Interval ◽  
The Tibetan Plateau ◽  
Full Time ◽  
Glacial Lakes ◽  
Southeastern Tibetan Plateau

Abstract. Glacial lakes are important component of the cryosphere in the Tibetan Plateau. In response to climate warming, they threaten the downstream lives, ecological environment and public infrastructures through outburst floods in a short time. Although most of the efforts have been made to extract glacial lake outlines and detect their changes with remotely sensed images, the temporal frequency and spatial resolution of glacial lake datasets are generally not fine enough to reflect the detailed process of glacial lake dynamics, especially for potentially dangerous glacial lakes with high-frequency variability. By using a full time-series Sentinel-1A/1B imagery during a year, this study presents a new systematic method to extract the glacial lake outlines with fast variability in southeastern Tibetan Plateau at the time interval of six days. Our approach was based on the level-set segmentation, combined with a median pixel compositing of SAR backscattering coefficients stacks as regularization term, to robustly estimate the lake extent across the observed time range. The mapping results were validated against with manually digitized lake outlines derived from GF-2 PMS imagery, with the overall accuracy and Kappa coefficient of 96.54 % and 0.95, respectively. In comparison with results from classical supervised SVM and unsupervised ISODATA methods, the proposed method proves to be much more robust and effective to detect glacial lakes with irregular boundaries and that have similar backscattering with surroundings. This study also demonstrates the feasibility of time-series Sentinel-1A/1B SAR data in continuous monitoring of glacial lake outline dynamics.

scholarly journals Prediction of Transient NOx Emission from Diesel Vehicles Based on Deep-Learning Differentiation Model with Double Noise Reduction

Atmosphere ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1702
Author(s):  
Jiaqiang Li ◽  
Yang Yu ◽  
Yanyan Wang ◽  
Longqing Zhao ◽  
Chao He
Keyword(s):  
Time Series ◽  
Diesel Engines ◽  
High Frequency ◽  
Singular Spectrum Analysis ◽  
Low Frequency ◽  
Ensemble Empirical Mode Decomposition ◽  
Support Vector ◽  
Nox Emissions ◽  
Diesel Vehicles ◽  
Original Time

For diesel engines, accurate prediction of NOx (Nitrogen Oxides) emission plays an essential role in virtual NOx sensor development and engine design under situations of actual road driving. However, due to the randomness and uncertainty in the driving process of diesel vehicles, it is difficult to make predictions about NOx emissions. In order to solve this problem, this paper proposes differential models for noise reductions of NOx emissions in time series. First, according to the internal fluctuation of time series, use SSA (Singular Spectrum Analysis) to reduce the noises of the original time series; second, use ICEEMDAN (Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) to decompose the noise-reducing data into several relatively stable subsequences; third, use the sample entropy to calculate the complexity of each subsequence, and divide the sequences into high-frequency ones and low-frequency ones; finally, use GRU (Gated Recurrent Unit) to complete the prediction of high-frequency sequences and SVR (Support Vector Regression) for the prediction of low-frequency sequences. To obtain the final models, integrate the prediction results of the subsequences. Make comparisons with five single models, SSA single-processing models, and ICEEMDAN single-processing models. The experimental results show that the proposed model can predict the instantaneous NOx emissions of diesel engines better than the single model and the model processed by SSA, and the differentiated model can effectively improve the execution speed of the model.

scholarly journals Rice-Field Mapping with Sentinel-1A SAR Time-Series Data

2020 ◽  
Vol 13 (1) ◽  
pp. 103
Author(s):  
Lena Chang ◽  
Yi-Ting Chen ◽  
Jung-Hua Wang ◽  
Yang-Lang Chang
Keyword(s):  
Time Series ◽  
Detection Efficiency ◽  
Experimental Results ◽  
The Other ◽  
Series Data ◽  
Support Vector ◽  
Time Interval ◽  
Detection Accuracy ◽  
Data Set ◽  
Decision Method

This study proposed a feature-based decision method for the mapping of rice cultivation by using the time-series C-band synthetic aperture radar (SAR) data provided by Sentinel-1A. In this study, a model related to crop growth was first established. The model was developed based on a cubic polynomial function which was fitted by the complete time-series SAR backscatters during the rice growing season. From the developed model, five rice growth-related features were introduced, including backscatter difference (BD), time interval (TI) between vegetative growth and maturity stages, backscatter variation rate (BVR), average normalized backscatter (ANB) and maximum backscatter (MB). Then, a decision method based on the combination of the five extracted features was proposed to improve the rice detection accuracy. In order to verify the detection performance of the proposed method, the test data set of this study consisted of 50,000 rice and non-rice fields which were randomly sampled from a research area in Taiwan for simulation verification. From the experimental results, the proposed method can improve overall accuracy in rice detection by 6% compared with the method using feature BD. Furthermore, the rice detection efficiency of the proposed method was compared with other four classifiers, including decision tree (DT), support vector machine (SVM), K-nearest neighbor (KNN) and quadratic discriminant analysis (QDA). The experimental results show that the proposed method has better rice detection accuracy than the other four classifiers, with an overall accuracy of 91.9%. This accuracy is 3% higher than fine SVM, which performs best among the other four classifiers. In addition, the consistency and effectiveness of the proposed method in rice detection have been verified for different years and studied regions.

scholarly journals Glacial Lakes Mapping Using Multi Satellite PlanetScope Imagery and Deep Learning

2020 ◽  
Vol 9 (10) ◽  
pp. 560
Author(s):  
Nida Qayyum ◽  
Sajid Ghuffar ◽  
Hafiz Ahmad ◽  
Adeel Yousaf ◽  
Imran Shahid
Keyword(s):  
Deep Learning ◽  
Surface Water ◽  
Temporal Resolution ◽  
Learning Model ◽  
Glacial Lake ◽  
Support Vector ◽  
Glacial Lakes ◽  
Outburst Flood ◽  
Small Satellites ◽  
Deep Learning Model

Glacial lakes mapping using satellite remote sensing data are important for studying the effects of climate change as well as for the mitigation and risk assessment of a Glacial Lake Outburst Flood (GLOF). The 3U cubesat constellation of Planet Labs offers the capability of imaging the whole Earth landmass everyday at 3–4 m spatial resolution. The higher spatial, as well as temporal resolution of PlanetScope imagery in comparison with Landsat-8 and Sentinel-2, makes it a valuable data source for monitoring the glacial lakes. Therefore, this paper explores the potential of the PlanetScope imagery for glacial lakes mapping with a focus on the Hindu Kush, Karakoram and Himalaya (HKKH) region. Though the revisit time of the PlanetScope imagery is short, courtesy of 130+ small satellites, this imagery contains only four bands and the imaging sensors in these small satellites exhibit varying spectral responses as well as lower dynamic range. Furthermore, the presence of cast shadows in the mountainous regions and varying spectral signature of the water pixels due to differences in composition, turbidity and depth makes it challenging to automatically and reliably extract surface water in PlanetScope imagery. Keeping in view these challenges, this work uses state of the art deep learning models for pixel-wise classification of PlanetScope imagery into the water and background pixels and compares the results with Random Forest and Support Vector Machine classifiers. The deep learning model is based on the popular U-Net architecture. We evaluate U-Net architecture similar to the original U-Net as well as a U-Net with a pre-trained EfficientNet backbone. In order to train the deep neural network, ground truth data are generated by manual digitization of the surface water in PlanetScope imagery with the aid of Very High Resolution Satellite (VHRS) imagery. The created dataset consists of more than 5000 water bodies having an area of approx. 71km2 in eight different sites in the HKKH region. The evaluation of the test data show that the U-Net with EfficientNet backbone achieved the highest F1 Score of 0.936. A visual comparison with the existing glacial lake inventories is then performed over the Baltoro glacier in the Karakoram range. The results show that the deep learning model detected significantly more lakes than the existing inventories, which have been derived from Landsat OLI imagery. The trained model is further evaluated on the time series PlanetScope imagery of two glacial lakes, which have resulted in an outburst flood. The output of the U-Net is also compared with the GLakeMap data. The results show that the higher spatial and temporal resolution of PlanetScope imagery is a significant advantage in the context of glacial lakes mapping and monitoring.

scholarly journals Spatio-temporal evolution of glacial lakes in the Tibetan Plateau over the past 30 years

2021 ◽  
Author(s):  
Xiangyang Dou ◽  
Xuanmei Fan ◽  
Ali P. Yunus ◽  
Junlin Xiong ◽  
Ran Tang ◽  
...  
Keyword(s):  
Climate Change ◽  
Tibetan Plateau ◽  
Temporal Evolution ◽  
Glacial Lake ◽  
Freshwater Ecosystem ◽  
The Tibetan Plateau ◽  
Glacial Lakes ◽  
The Third ◽  
Multi Temporal ◽  
Spatio Temporal

Abstract. As the Third Pole of the Earth and the Water Tower of Asia, Tibetan Plateau (TP) nurtures large numbers of glacial lakes, which are sensitive to global climate change. These lakes modulate the freshwater ecosystem in the region, but concurrently pose severe threats to the valley population by means of sudden glacial lake outbursts and consequent floods (GLOFs). Lack of high-resolution multi-temporal inventory of glacial lakes in TP hampers a better understanding and prediction of the future trend and risk of glacial lakes. Here, we created a multi-temporal inventory of glacial lakes in TP using 30 years record of satellite images (1990–2019), and discussed their characteristics and spatio-temporal evolution over the years. Results showed that their number and area had increased by 3285 and 258.82 km2, respectively in the last 3 decades. We noticed that different regions of TP exhibited varying change rates in glacial lake size; some regions even showed decreasing trend such as the western Pamir and the eastern Hindu Kush because of reduced rainfall rates. The mapping uncertainty is about 17.5 %, lower than other available datasets, thus making our inventory, a reliable one for the spatio-temporal evolution analysis of glacial lakes in TP. Our lake inventory data are freely available at https://doi.org/10.5281/zenodo.5574289 (Dou et al., 2021); it can help to study climate change-glacier-glacial lake-GLOF interactions in the third pole and serve input to various hydro-climatic studies.

scholarly journals GLACIAL LAKE EVOLUTION BASED ON REMOTE SENSING TIME SERIES: A CASE STUDY OF TSHO ROLPA IN NEPAL

2020 ◽  
Vol V-3-2020 ◽  
pp. 633-639
Author(s):  
M. V. Peppa ◽  
S. B. Maharjan ◽  
S. P. Joshi ◽  
W. Xiao ◽  
J. P. Mills
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Glacial Lake ◽  
Lake Area ◽  
Glacial Lakes ◽  
Glacier Terminus ◽  
Lake Evolution ◽  
Frequency Monitoring ◽  
Downstream Communities ◽  
Sensing Time

Abstract. Himalayan glaciers have retreated rapidly in recent years. Resultant glacial lakes in the region pose potential catastrophic threats to downstream communities, especially under a changing climate. The potential for Glacial Lake Outburst Floods (GLOFs) has increased and studies have assessed the risks of those in Nepal and prioritised several glacial lakes for urgent and closer investigation. The risk posed by the Tsho Rolpa Glacial Lake is one of the most serious in Nepal. To investigate the feasibility of high-frequency monitoring of glacial lake evolution by remote sensing, this paper proposes a workflow for automated glacial lake boundary extraction and evolution using a time series of Sentinel optical imagery. The waterbody is segmented and vectorised using bimodal histograms from water indices. The vectorised lake boundary is validated against reference data extracted from rigorous contemporary unmanned aerial vehicle (UAV)-based photogrammetric survey. Lake boundaries were subsequently extracted at four different epochs to evaluate the evolution of the lake, especially at the glacier terminus. The final lake area was estimated at 1.61 km2, significantly larger than the areal extent last formally reported. A 0.99 m/day maximum, and a 0.45 m/day average, horizontal glacier retreat rates were estimated. The reported research has demonstrated the potential of remote sensing time series to monitor glacial lake evolution, which is particularly important for lakes in remote mountain regions that are otherwise difficult to access.

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