scholarly journals Gravel Mining Activity Impact on Riverbed Erosion and Bridge Foundation Stability

2021 ◽  
Vol 54 (2C) ◽  
pp. 68-79
Author(s):  
Ayad Ali Faris Beg ◽  
Mohammed Bahjat Thamer ◽  
Alyaa Gatea Shiltagh ◽  
Ahmed H. Al-Sulttani
Keyword(s):  
Field Survey ◽  
Single Channel ◽  
Remote Sensing Data ◽  
Flow Speed ◽  
Google Earth ◽  
Gravel Mining ◽  
Failure Event ◽  
Bridge Foundation ◽  
Anabranching River ◽  
Scs Cn

Geomorphological processes pose a risk that deserves attention and planning to avoid that, especially in the section near to east of Tuz bridge. This section of the valley facing a dramatic increase in gravel excavation and sorting of aggregates, consequence led to a change in the pattern of river branches flow from an anabranching river to a single-channel river, which led to a concentration of river discharge during floods. On 9th December 2018, Tuz Bridge was failed due to a heavy rainstorm three days preceding the failure event. The current study aims to conduct a field survey of all the human activities in the study area to assess river changes from remote sensing data the amount of runoff and river peak discharge based on rainfall data using SCS-CN method. In this study, ArcGIS, ArcGIS Earth, Google Earth, and WMS software are incorporated in the data analysis. The revealed results indicate the severe modification of valley morphology and converting the river pattern to flow during flood within a single channel with flow speed exceeded the critical velocity to induce vertical erosion of gravel and sands under the foundations of the bridge and causing the displacement and settlement of the bridge. The study recommends the local administration prevent gravel mining from the river valley at the upstream area of the bridge

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 Integrating GIS-Based MCDA Techniques and the SCS-CN Method for Identifying Potential Zones for Rainwater Harvesting in a Semi-Arid Area

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 704
Author(s):  
Hussein Al-Ghobari ◽  
Ahmed Z. Dewidar
Keyword(s):  
Water Conservation ◽  
Surface Runoff ◽  
Rainwater Harvesting ◽  
Global Climate ◽  
Remote Sensing Data ◽  
Curve Number ◽  
Drainage Density ◽  
Promising Alternative ◽  
Thematic Layers ◽  
Scs Cn

An increasing scarcity of water, as well as rapid global climate change, requires more effective water conservation alternatives. One promising alternative is rainwater harvesting (RWH). Nevertheless, the evaluation of RWH potential together with the selection of appropriate sites for RWH structures is significantly difficult for the water managers. This study deals with this difficulty by identifying RWH potential areas and sites for RWH structures utilizing geospatial and multi-criteria decision analysis (MCDA) techniques. The conventional data and remote sensing data were employed to set up needed thematic layers using ArcGIS software. The soil conservation service curve number (SCS-CN) method was used to determine surface runoff, centered on which yearly runoff potential map was produced in the ArcGIS environment. Thematic layers such as drainage density, slope, land use/cover, and runoff were allotted appropriate weights to produced RWH potential areas and zones appropriate for RWH structures maps of the study location. Results analysis revealed that the outcomes of the spatial allocation of yearly surface runoff depth ranging from 83 to 295 mm. Moreover, RWH potential areas results showed that the study areas can be categorized into three RWH potential areas: (a) low suitability, (b) medium suitability, and (c) high suitability. Nearly 40% of the watershed zone falls within medium and high suitability RWH potential areas. It is deduced that the integrated MCDA and geospatial techniques provide a valuable and formidable resource for the strategizing of RWH within the study zones.

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 PEMETAAN ALIH FUNGSI LAHAN SAWAH KE NON SAWAH DAN DAMPAKNYA TERHADAP PRODUKSI GABAH DI KECAMATAN KEBAKKRAMAT, KARANGANYAR

2009 ◽  
Vol 24 (1) ◽  
pp. 1
Author(s):  
Mujiyo Mujiyo
Keyword(s):  
Rice Field ◽  
Field Survey ◽  
Google Earth ◽  
Land Use Type ◽  
Grain Production ◽  
Land Productivity ◽  
Internet Media ◽  
Increasing Trend ◽  
The Relationship ◽  
Made In

<span>The research has aims to know ; (1) the velocity of function displace of the rice field to the non rice field during year period 2000, 2004 and 2008, (2) change grain product during year period 2000, 2004 and 2008, and (3) the relationship between the velocity of function displace of the rice field to the non rice field and the grain production during year period 2000, 2004 and 2008. Function displace of the rice field in the Kebabakkramat District known by determining the wide of each land use type in the appointed year, and then comparing it with data in the next year. The first mapping was based on the Map of Rupa Bumi Bakosurtanal which made in 2000. The second mapping was based on the image QUICK BIRD 2004 which published in the internet media Google Earth. And the third mapping determined by field survey in the 2008. Result of the research shows that ; (1) rice field in the Kebakkramat District along period 2000 until 2008 had function displace, 2.571,89 ha (2000) decreased to become 2.153,33 ha (2004), and decreased again to become 2.128,11 ha (2008), (2) grain production in the Kebakkramat District along period 2000 until 2007 shows increasing trend, 39.880 ton (2000) increased to become 40.631 ton (2003), but decreased to become 35.354 ton (2004), and increased again to become 43.062 ton (2007), (3) although wide of the rice field decreased, but the grain production increased, because increasing its land productivity caused by continuity of the rice field intensification program.</span>

scholarly journals Modelling Free Flow Speed on Two-Lane Rural Highways in Bosnia and Herzegovina

2014 ◽  
Vol 26 (2) ◽  
pp. 121-127 ◽  
Author(s):  
Ivan Lovrić ◽  
Dražen Cvitanić ◽  
Deana Breški
Keyword(s):  
Field Survey ◽  
Evaluation Model ◽  
Practical Method ◽  
Flow Speed ◽  
Free Flow ◽  
Rural Highways ◽  
Speed Flow ◽  
Free Flow Speed ◽  
Flow Diagrams ◽  
Analysis Models

Free flow speed is used as a parameter in transportation planning and capacity analysis models, as well as speed-flow diagrams. Many of these models suggest estimating free flow speed according to measurements from similar highways, which is not a practical method for use in B&H. This paper first discusses problems with using these methodologies in conditions prevailing in B&H and then presents a free flow speed evaluation model developed from a comprehensive field survey conducted on nine homogeneous sections of state and regional roads.

scholarly journals High-Resolution Vegetation Mapping Using eXtreme Gradient Boosting Based on Extensive Features

2019 ◽  
Vol 11 (12) ◽  
pp. 1505 ◽  
Author(s):  
Heng Zhang ◽  
Anwar Eziz ◽  
Jian Xiao ◽  
Shengli Tao ◽  
Shaopeng Wang ◽  
...  
Keyword(s):  
New Zealand ◽  
High Resolution ◽  
Large Scale ◽  
Field Survey ◽  
Terrestrial Ecosystems ◽  
Google Earth ◽  
Gradient Boosting ◽  
Global Changes ◽  
Vegetation Map ◽  
Extreme Gradient Boosting

Accurate mapping of vegetation is a premise for conserving, managing, and sustainably using vegetation resources, especially in conditions of intensive human activities and accelerating global changes. However, it is still challenging to produce high-resolution multiclass vegetation map in high accuracy, due to the incapacity of traditional mapping techniques in distinguishing mosaic vegetation classes with subtle differences and the paucity of fieldwork data. This study created a workflow by adopting a promising classifier, extreme gradient boosting (XGBoost), to produce accurate vegetation maps of two strikingly different cases (the Dzungarian Basin in China and New Zealand) based on extensive features and abundant vegetation data. For the Dzungarian Basin, a vegetation map with seven vegetation types, 17 subtypes, and 43 associations was produced with an overall accuracy of 0.907, 0.801, and 0.748, respectively. For New Zealand, a map of 10 habitats and a map of 41 vegetation classes were produced with 0.946, and 0.703 overall accuracy, respectively. The workflow incorporating simplified field survey procedures outperformed conventional field survey and remote sensing based methods in terms of accuracy and efficiency. In addition, it opens a possibility of building large-scale, high-resolution, and timely vegetation monitoring platforms for most terrestrial ecosystems worldwide with the aid of Google Earth Engine and citizen science programs.

scholarly journals TCDNet: Trilateral Change Detection Network for Google Earth Image

2020 ◽  
Vol 12 (17) ◽  
pp. 2669
Author(s):  
Junhao Qian ◽  
Min Xia ◽  
Yonghong Zhang ◽  
Jia Liu ◽  
Yiqing Xu
Keyword(s):  
Deep Learning ◽  
Change Detection ◽  
Remote Sensing Data ◽  
Semantic Segmentation ◽  
Google Earth ◽  
Detection Methods ◽  
Global Information ◽  
Supervised Methods ◽  
Image Pairs ◽  
Main Module

Change detection is a very important technique for remote sensing data analysis. Its mainstream solutions are either supervised or unsupervised. In supervised methods, most of the existing change detection methods using deep learning are related to semantic segmentation. However, these methods only use deep learning models to process the global information of an image but do not carry out specific trainings on changed and unchanged areas. As a result, many details of local changes could not be detected. In this work, a trilateral change detection network is proposed. The proposed network has three branches (a main module and two auxiliary modules, all of them are composed of convolutional neural networks (CNNs)), which focus on the overall information of bitemporal Google Earth image pairs, the changed areas and the unchanged areas, respectively. The proposed method is end-to-end trainable, and each component in the network does not need to be trained separately.

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.

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