Debris-induced stagnation and ensuing morphological evolution of a central Himalayan glacier

2021 ◽  
Author(s):  
Purushottam Kumar Garg ◽  
Aparna Shukla ◽  
Santosh Kumar Rai ◽  
Jairam Singh Yadav
Keyword(s):  
Remote Sensing ◽  
Morphological Evolution ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Lateral Moraine ◽  
Central Himalaya ◽  
Ablation Zone ◽  
Glacier Surface ◽  
Glacier System ◽  
Debris Cover

<p>This study presents field evidences (October 2018) and remote sensing measurements (2000-2020) to show stagnant conditions of lower ablation zone (LAZ) of the ‘companion glacier’, central Himalaya, India and its implication on the morphological evolution. The Companion glacier is named so as it accompanied the Chorabari glacier (widely studied benchmark glacier in the central Himalaya) in the distant past. Supraglacial debris thickness, supraglacial ponds anf other morphological features (e.g. lateral moraine height, supraglacial mounds) were measured/observed in the field. Glacier area, length, debris extent, surface elevation change and surface ice velocity were estimated using satellite remote sensing data from Landsat-TM/ETM+/OLI, Sentinel-MSI, Terra-ASTER and SRTM, Cartosat-1 and Google Earth images. Results show that the glacier has very small accumulation area and it is mainly fed by avalanches. The headwall of glacier is very steep which causes frequent avalanches leading to voluminous debris addition to the glacier system. Consequently, about 80% area of the glacier is debris-covered. The debris is very thick in the LAZ exceeding several meters in the LAZ and comprised of big boulders making debris thickness measurements practically impossible particularly in the snout region. However, debris thickness decreases with increasing distance from the snout and is in the order of 20-40 cm at about 2.5 km upglacier. The huge debris cover has protected the glacier ice from rapid melting. That’s why surface lowering of the glacier is less as compared to nearby Chorabari glacier. Moreover, due to (a) less mass supply from upper reaches and (b) huge debris cover, the glacier movement is very slow. The movement is too low that is allowed vegetation (some big grasses with wooded stems) to grow and survive on the glacier surface. The slow moving LAZ also causing bulging on the upper ablation zone (UAZ). Consequently, several mounds have developed on the UAZ. Thin debris slides down from mounds exposing the ice underneath for melting. Owing to these processes, spot melting is now a dominant mechanism of glacier wastage in the companion glacier. Thus, it can be summarized that careful field observations along with remote sensing estimates can be very important for understanding the glacier evolution.</p>

scholarly journals Assessing the Effect of Training Sampling Design on the Performance of Machine Learning Classifiers for Land Cover Mapping Using Multi-Temporal Remote Sensing Data and Google Earth Engine

2021 ◽  
Vol 13 (8) ◽  
pp. 1433
Author(s):  
Shobitha Shetty ◽  
Prasun Kumar Gupta ◽  
Mariana Belgiu ◽  
S. K. Srivastav
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Random Sampling ◽  
Sampling Design ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Multi Temporal ◽  
Google Earth Engine

Machine learning classifiers are being increasingly used nowadays for Land Use and Land Cover (LULC) mapping from remote sensing images. However, arriving at the right choice of classifier requires understanding the main factors influencing their performance. The present study investigated firstly the effect of training sampling design on the classification results obtained by Random Forest (RF) classifier and, secondly, it compared its performance with other machine learning classifiers for LULC mapping using multi-temporal satellite remote sensing data and the Google Earth Engine (GEE) platform. We evaluated the impact of three sampling methods, namely Stratified Equal Random Sampling (SRS(Eq)), Stratified Proportional Random Sampling (SRS(Prop)), and Stratified Systematic Sampling (SSS) upon the classification results obtained by the RF trained LULC model. Our results showed that the SRS(Prop) method favors major classes while achieving good overall accuracy. The SRS(Eq) method provides good class-level accuracies, even for minority classes, whereas the SSS method performs well for areas with large intra-class variability. Toward evaluating the performance of machine learning classifiers, RF outperformed Classification and Regression Trees (CART), Support Vector Machine (SVM), and Relevance Vector Machine (RVM) with a >95% confidence level. The performance of CART and SVM classifiers were found to be similar. RVM achieved good classification results with a limited number of training samples.

scholarly journals Agricultural Expansion in Mato Grosso from 1986–2000: A Bayesian Time Series Approach to Tracking Past Land Cover Change

2020 ◽  
Vol 12 (4) ◽  
pp. 688 ◽  
Author(s):  
Jacky Lee ◽  
Jeffrey A. Cardille ◽  
Michael T. Coe
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Land Cover ◽  
Spatial Resolution ◽  
Processing Speed ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Wide Audience ◽  
Data Set ◽  
Mato Grosso

Landsat 5 has produced imagery for decades that can now be viewed and manipulated in Google Earth Engine, but a general, automated way of producing a coherent time series from these images—particularly over cloudy areas in the distant past—is elusive. Here, we create a land use and land cover (LULC) time series for part of tropical Mato Grosso, Brazil, using the Bayesian Updating of Land Cover: Unsupervised (BULC-U) technique. The algorithm built backward in time from the GlobCover 2009 data set, a multi-category global LULC data set at 300 m resolution for the year 2009, combining it with Landsat time series imagery to create a land cover time series for the period 1986–2000. Despite the substantial LULC differences between the 1990s and 2009 in this area, much of the landscape remained the same: we asked whether we could harness those similarities and differences to recreate an accurate version of the earlier LULC. The GlobCover basis and the Landsat-5 images shared neither a common spatial resolution nor time frame, But BULC-U successfully combined the labels from the coarser classification with the spatial detail of Landsat. The result was an accurate fine-scale time series that quantified the expansion of deforestation in the study area, which more than doubled in size during this time. Earth Engine directly enabled the fusion of these different data sets held in its catalog: its flexible treatment of spatial resolution, rapid prototyping, and overall processing speed permitted the development and testing of this study. Many would-be users of remote sensing data are currently limited by the need to have highly specialized knowledge to create classifications of older data. The approach shown here presents fewer obstacles to participation and allows a wide audience to create their own time series of past decades. By leveraging both the varied data catalog and the processing speed of Earth Engine, this research can contribute to the rapid advances underway in multi-temporal image classification techniques. Given Earth Engine’s power and deep catalog, this research further opens up remote sensing to a rapidly growing community of researchers and managers who need to understand the long-term dynamics of terrestrial systems.

scholarly journals Garlic and Winter Wheat Identification Based on Active and Passive Satellite Imagery and the Google Earth Engine in Northern China

2020 ◽  
Vol 12 (21) ◽  
pp. 3539
Author(s):  
Haifeng Tian ◽  
Jie Pei ◽  
Jianxi Huang ◽  
Xuecao Li ◽  
Jian Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Winter Wheat ◽  
Satellite Imagery ◽  
Remote Sensing Data ◽  
Northern China ◽  
Google Earth ◽  
Kappa Coefficient ◽  
Landsat 8 ◽  
Winter Crops ◽  
Sentinel 2

Garlic and winter wheat are major economic and grain crops in China, and their boundaries have increased substantially in recent decades. Updated and accurate garlic and winter wheat maps are critical for assessing their impacts on society and the environment. Remote sensing imagery can be used to monitor spatial and temporal changes in croplands such as winter wheat and maize. However, to our knowledge, few studies are focusing on garlic area mapping. Here, we proposed a method for coupling active and passive satellite imagery for the identification of both garlic and winter wheat in Northern China. First, we used passive satellite imagery (Sentinel-2 and Landsat-8 images) to extract winter crops (garlic and winter wheat) with high accuracy. Second, we applied active satellite imagery (Sentinel-1 images) to distinguish garlic from winter wheat. Third, we generated a map of the garlic and winter wheat by coupling the above two classification results. For the evaluation of classification, the overall accuracy was 95.97%, with a kappa coefficient of 0.94 by eighteen validation quadrats (3 km by 3 km). The user’s and producer’s accuracies of garlic are 95.83% and 95.85%, respectively; and for the winter wheat, these two accuracies are 97.20% and 97.45%, respectively. This study provides a practical exploration of targeted crop identification in mixed planting areas using multisource remote sensing data.

Automated Multi-Sensor Near-Real Time Flood Monitoring in the Lower Mekong

2020 ◽  
Author(s):  
Amanda Markert ◽  
Kel Markert ◽  
Timothy Mayer ◽  
Farrukh Chisthie ◽  
Biplov Bhandari Bhandari ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Southeast Asia ◽  
Real Time ◽  
Disaster Response ◽  
Monsoon Season ◽  
Remote Sensing Data ◽  
Microwave Remote Sensing ◽  
Cost Effective ◽  
Google Earth ◽  
Flood Maps

<p>Floods and water-related disasters impact local populations across many regions in Southeast Asia during the annual monsoon season.  Satellite remote sensing serves as a critical resource for generating flood maps used in disaster efforts to evaluate flood extent and monitor recovery in remote and isolated regions where information is limited.  However, these data are retrieved by multiple sensors, have varying latencies, spatial, temporal, and radiometric resolutions, are distributed in different formats, and require different processing methods making it difficult for end-users to use the data.  SERVIR-Mekong has developed a near real-time flood service, HYDRAFloods, in partnership with Myanmar’s Department of Disaster Management that leverages Google Earth Engine and cloud computing to generate automated multi-sensor flood maps using the most recent imagery available of affected areas. The HYDRAFloods application increases the spatiotemporal monitoring of hydrologic events across large areas by leveraging optical, SAR, and microwave remote sensing data to generate flood water extent maps.  Beta testing of HYDRFloods conducted during the 2019 Southeast Asia monsoon season emphasized the importance of multi-sensor observations as frequent cloud cover limited useable imagery for flood event monitoring. Given HYDRAFloods’ multi-sensor approach, cloud-based resources offer a means to consolidate and streamline the process of accessing, processing, and visualizing flood maps in a more cost effective and computationally efficient way. The HYDRAFlood’s cloud-based approach enables a consistent, automated methodology for generating flood extent maps that are made available through a single, tailored, mapviewer that has been customized based on end-user feedback, allowing users to switch their focus to using data for disaster response.</p>

scholarly journals Impact of varying debris cover thickness on ablation: a case study for Koxkar Glacier in the Tien Shan

2014 ◽  
Vol 8 (2) ◽  
pp. 377-386 ◽  
Author(s):  
M. Juen ◽  
C. Mayer ◽  
A. Lambrecht ◽  
H. Han ◽  
S. Liu
Keyword(s):  
Remote Sensing ◽  
Field Measurements ◽  
Tien Shan ◽  
Shielding Effect ◽  
Glacier Surface ◽  
Glacier Terminus ◽  
Covered Area ◽  
Debris Cover ◽  
High Resolution Satellite Imagery ◽  
Cover Thickness

Abstract. To quantify the ablation processes on a debris covered glacier, a simple distributed ablation model has been developed and applied to a selected glacier. For this purpose, a set of field measurements was carried out to collect empirical data. A morphometric analysis of the glacier surface enables us to capture statistically the areal distribution of topographic features that influence debris thickness and consequently ablation. Remote-sensing techniques, using high-resolution satellite imagery, were used to extrapolate the in situ point measurements to the whole ablation area and to map and classify melt-relevant surface types. As a result, a practically applicable method is presented that allows the estimation of ablation on a debris covered glacier by combining field data and remote-sensing information. The sub-debris ice ablation accounts for about 24% of the entire ice ablation, while the percentage of the moraine covered area accounts for approximately 32% of the entire glacierized area. Although the ice cliffs occupy only 1.7% of the debris covered area, the melt amount accounts for approximately 12% of the total sub-debris ablation and 2.5% of the total ablation respectively. Our study highlights the influence of debris cover on the response of the glacier terminus in a particular climate setting. Due to the fact that melt rates beyond 0.1 m of moraine cover are highly restricted, the shielding effect of the debris cover dominates over the temperature and elevation dependence of the ablation in the bare ice case.

Climate Engine: Cloud Computing and Visualization of Climate and Remote Sensing Data for Advanced Natural Resource Monitoring and Process Understanding

2017 ◽  
Vol 98 (11) ◽  
pp. 2397-2410 ◽  
Author(s):  
Justin L. Huntington ◽  
Katherine C. Hegewisch ◽  
Britta Daudert ◽  
Charles G. Morton ◽  
John T. Abatzoglou ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Cloud Computing ◽  
Big Data ◽  
Natural Resource ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Resource Monitoring ◽  
Sensing Data ◽  
Process Understanding ◽  
Natural Resource Monitoring

Abstract The paucity of long-term observations, particularly in regions with heterogeneous climate and land cover, can hinder incorporating climate data at appropriate spatial scales for decision-making and scientific research. Numerous gridded climate, weather, and remote sensing products have been developed to address the needs of both land managers and scientists, in turn enhancing scientific knowledge and strengthening early-warning systems. However, these data remain largely inaccessible for a broader segment of users given the computational demands of big data. Climate Engine (http://ClimateEngine.org) is a web-based application that overcomes many computational barriers that users face by employing Google’s parallel cloud-computing platform, Google Earth Engine, to process, visualize, download, and share climate and remote sensing datasets in real time. The software application development and design of Climate Engine is briefly outlined to illustrate the potential for high-performance processing of big data using cloud computing. Second, several examples are presented to highlight a range of climate research and applications related to drought, fire, ecology, and agriculture that can be rapidly generated using Climate Engine. The ability to access climate and remote sensing data archives with on-demand parallel cloud computing has created vast opportunities for advanced natural resource monitoring and process understanding.

scholarly journals Spatial Analysis of The Needs of Green Open Space at Universitas Negeri Padang

2017 ◽  
Vol 1 (2) ◽  
pp. 140
Author(s):  
Muhammad Aliman ◽  
Arisius Yustesia ◽  
Eri Barlian ◽  
Nurhasan Syah
Keyword(s):  
Remote Sensing ◽  
Spatial Analysis ◽  
Environmental Quality ◽  
Open Space ◽  
Green Space ◽  
Ornamental Plants ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Minimum Width ◽  
Clean Air

The decreasing of environmental quality in padang is caused by the changes of the land, the increasing number of vehicles and the increasing number of population. The solution to overcome these problems is by providing a green open space at universitas negeri padang (unp). The objectives of this study are 1) to analyze the needs of green open space at unp, 2) to plan the construction of open green space at unp. The method employed in this study was survey by using spatial analysis remote sensing data from google earth. The results of the study revealed that unp had open green space as large as 7.643 ha. The area of green open space at unp that fulfilled the width criteria was as much as 10%, and the fulfillment of population and clean air criteria was as much as 20%. However, the minimum width criterion of green open space, which was as much as 30%, was not fulfilled yet. The discrepancy between the area of open green space and the criteria of minimum width (30%) was 0.447 ha. Such lack of green open space can be filled by: optimizing the unoccupied land as large as 1.4 ha by planting the clump, providing 2308 flower’s pots, and making use of building shelter and building lobbies, and campus corridor to be planted with clump, ornamental plants or other types of epiphytes and lianas.

scholarly journals Tree Species Distribution Change Study in Mount Tai Based on Landsat Remote Sensing Image Data

Forests ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 130 ◽  
Author(s):  
Yan Meng ◽  
Banghua Cao ◽  
Peili Mao ◽  
Chao Dong ◽  
Xidong Cao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Species Distribution ◽  
Tree Species ◽  
Black Locust ◽  
Robinia Pseudoacacia ◽  
Remote Sensing Data ◽  
Image Data ◽  
Google Earth ◽  
Percentage Decrease ◽  
Forestry Resources

Located in the Mount Tai state-owned forest farm, this study adopted Landsat multispectral remote sensing data in 2000 and 2016 on the GEE (Google Earth Engine) platform and selected four phases of images each year according to the phenological period. By dealing with the current situation map of forestry resources in 2000 and the field survey data in 2016, the samples of tree species distribution in 2000 and 2016 were obtained. On the basis of topographic correction with the empirical rotation model, this study used the random forest (RF) classifier to classify tree species from remote sensing images in 2000 and 2016, achieving high classification accuracy. The results showed that, after 16 years of evolution, the percentage of pine species in the forest decreased from 55.69% to 50.22%, with a percentage decrease as high as 5.47%. The percentage of black locust (Robinia pseudoacacia) increased from 10.15% in 2000 to 13.75% in 2016, with an increase of 3.60%. Quercus also had a positive growth in the area. This result reflected the expansion of black locust.

Effect of Locust Invasion and Mitigation Using Remote Sensing Techniques: A Case Study of North Sindh Pakistan

2022 ◽  
Vol 88 (1) ◽  
pp. 47-53
Author(s):  
Muhammad Nasar Ahmad ◽  
Zhenfeng Shao ◽  
Orhan Altan
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Landsat 8 ◽  
Abrupt Decrease ◽  
Computing Platform ◽  
Remote Sensing Techniques ◽  
Processing Techniques

This study comprises the identification of the locust outbreak that happened in February 2020. It is not possible to conduct ground-based surveys to monitor such huge disasters in a timely and adequate manner. Therefore, we used a combination of automatic and manual remote sensing data processing techniques to find out the aftereffects of locust attack effectively. We processed MODIS -normalized difference vegetation index (NDVI ) manually on ENVI and Landsat 8 NDVI using the Google Earth Engine (GEE ) cloud computing platform. We found from the results that, (a) NDVI computation on GEE is more effective, prompt, and reliable compared with the results of manual NDVI computations; (b) there is a high effect of locust disasters in the northern part of Sindh, Thul, Ghari Khairo, Garhi Yaseen, Jacobabad, and Ubauro, which are more vulnerable; and (c) NDVI value suddenly decreased to 0.68 from 0.92 in 2020 using Landsat NDVI and from 0.81 to 0.65 using MODIS satellite imagery. Results clearly indicate an abrupt decrease in vegetation in 2020 due to a locust disaster. That is a big threat to crop yield and food production because it provides a major portion of food chain and gross domestic product for Sindh, Pakistan.

Sign in / Sign up

Export Citation Format

Share Document