scholarly journals Assessment of the morphological trends and sediment dynamics in the Indus River, Pakistan

2021 ◽  
Author(s):  
Muhammad Waseem Boota ◽  
Chaode Yan ◽  
Muhammad Bilal Idrees ◽  
Ziwei Li ◽  
Shan-e-hyder Soomro ◽  
...  
Keyword(s):  
Sediment Load ◽  
Rough Set Theory ◽  
Velocity Shear ◽  
Low Flow ◽  
Landsat Images ◽  
Load Factors ◽  
Multi Temporal ◽  
High Flood ◽  
Peak Runoff ◽  
Bed Material

Abstract The Lower Indus reaches – Guddu and Sukkur – are among the most complicated areas in terms of reach migration. Both climate change and human activities in recent periods along with pond system operation accelerated riverine channel morphology. A GIS-based approach using multi-temporal Landsat images (1986–2020) was employed to characterize the morphometric parameters. Results showed that width of the study reaches varied from 2.1 to 12 km. The braiding index (BI) value for Guddu reach varied from 3.47 to 7.18, and BI value for Sukkur reach varied from 2.11 to 4.92. It is observed that no erosive activity of banks occurred for peak runoff value of <5,880 m3s−1. The sediment load during low flow (LF) period was estimated to be 0.715 million tons/day which comprised 77% fine sediment and 23% sand. The sediment load during high flood (HF) period was about 1.296 million tons/day. The median size (D50) of bed material during the HF period was 0.101–0.206 mm and during LF period was <0.0625 mm. The rough set theory (RST) showed that velocity, shear stress, slope, runoff, and sediment load factors are majorly causing the river shape changes. This study is a standpoint of planning flood recovery, riverine regulations, and navigation safety.

scholarly journals Appraisal of hydro-ecology, geomorphology, and sediment behavior during low and high floods in the Lower Indus River Estuary

2022 ◽  
Author(s):  
Muhammad Waseem Boota ◽  
Chaode Yan ◽  
Shan-e-hyder Soomro ◽  
Ziwei Li ◽  
Muhammad Zohaib ◽  
...  
Keyword(s):  
Sediment Load ◽  
Morphological Changes ◽  
River Estuary ◽  
Field Measurements ◽  
River System ◽  
Water Area ◽  
Low Flow ◽  
Indus River ◽  
Peak Runoff ◽  
Bed Material

Abstract The riverine ecosystem is beholden by the freshwater; however, morphological changes and sediment load destabilize the natural river system which deteriorates the ecology and geomorphology of the river ecosystem. The Lower Indus River Estuary (LIRE) geomorphological response was synthesized using satellite imagery (1986–2020) and evaluated against the field measurements. The estuary sinuosity index has an increasing trend from 1.84 (1986) to 1.92 (2020) and the estuary water area is increased from 101.41 km2 (1986) to 110.24 km2 (2020). The sediment load investigation at Kotri barrage indicated that the median size of bed material samples during the low-flow period falls between 0.100 and 0.203 mm and the bed material after the high flow has clay and silt (<0.0623 mm) ranging from 17–95% of the total weight of samples. The vegetated land loss on the banks is positively correlated with the peak runoff at Kotri barrage (r2=0.92). The bank erosion was computed with high precision (r2=0.84) based on an improved connection of the coefficient of erodibility and excess shear stress technique. This study will be helpful for policymakers to estimate the ecological health of LIRE, and sediment fluxes play an essential role in the mega-delta system and coastal management.

Stream-bed armouring under known conditions of upstream sediment input

1984 ◽  
Vol 21 (9) ◽  
pp. 1061-1066 ◽  
Author(s):  
Eric J. Schiller ◽  
A. Charles Rowney
Keyword(s):  
Particle Size ◽  
Sediment Load ◽  
Experimental Tests ◽  
Direct Influence ◽  
Sediment Sources ◽  
Characteristic Parameters ◽  
Bed Forms ◽  
Sediment Input ◽  
Bed Material ◽  
Stream Bed

Experiments were conducted to assess ways in which an imposed sediment load can affect the formation and final nature of an armoured bed. A flume loaded with a quartz aggregate of known composition was subjected to various sediment-laden flows of water to produce armoured beds. Characteristic parameters of the armoured beds were then compared.In general, it was found that the final armoured bed can be significantly altered by an imposed sediment load. As the size of the input sediment increased, the amount of bed material that was eroded, the resulting particle size of the bed, and the total roughness of the bed all decreased. The formation of bed forms was very important in this process. The trends observed in these experimental tests indicate that the presence or absence of upstream sediment sources has a direct influence on the resulting armoured layer.

Early stage variable rate nitrogen fertilization of silage maize driven by multi-temporal clustering of archive satellite data

2017 ◽  
Vol 8 (2) ◽  
pp. 288-292 ◽  
Author(s):  
R. Casa ◽  
F. Pelosi ◽  
S. Pascucci ◽  
F. Fontana ◽  
F. Castaldi ◽  
...  
Keyword(s):  
Satellite Data ◽  
Nitrogen Fertilization ◽  
Early Stage ◽  
Central Italy ◽  
N Fertilization ◽  
Yield Potential ◽  
Variable Rate ◽  
Landsat Images ◽  
Silage Maize ◽  
Multi Temporal

Nitrogen fertilization of silage maize in Central Italy is typically carried out with two applications at early stages of crop development: 2nd (V2) and 6th (V6) leaf respectively. In such conditions, the crop has not yet fully covered the soil and proximal or remote sensing of the canopy is hindered by the strong soil background signal. There is thus great interest in rapid and inexpensive approaches to N fertilization prescription. Therefore, an indirect method for inferring information on yield potential and soil variability, through a field-based clustering of multi-temporal satellite data, has been developed using archive Landsat images to identify temporally constant patterns. This method is potentially useful for the creation of prescription maps. The usefulness of the method was evaluated during an N fertilisation field trial in Maccarese (Central Italy), in 2016. At the V2 stage, both uniform and variable rate applications were performed and compared. A pseudo-cross variogram and a standardized ordinary co-kriging methodology was used to highlight spatially variable significant differences among the treatments.

scholarly journals A Classification of Landuse using time series analysis of Kota Tinggi District ,Johor Bahru, Malaysia.

2019 ◽  
Vol 4 (1) ◽  
pp. 61-63
Author(s):  
Alhaji Mustapha Isa
Keyword(s):  
Climate Change ◽  
Thematic Mapper ◽  
Present Condition ◽  
Landsat Images ◽  
The Past ◽  
Global Environmental ◽  
Multi Temporal ◽  
Ground Truthing ◽  
Confusion Matrices

Deforestation and climate change have become global environmental issues. The detection of forest changes in association with climate change can be successfully carried out by the use of multi-temporal remote sensing and modelling. This study undertook analysis of the past and present condition of the forest from the pattern changes of the Kota tinggi district johor state Malaysia, using landsat images of three different periods. These are thematic mapper (TM) data of 1998; enhanced thematic mapper (ETM+) image of 2008 and the operation land imager (OLI) of 2018 were collectively used. The images were geometrically and atmospherically pre-processed then classified, using maximum likelihood (M/C) algorithm to produce thematic land use/cover maps of the district. The accuracy of the classification was assessed through ground truthing and confusion matrices which revealed an accuracy of above 90% and kappa coefficient at 0.9 respectively.

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.

Quantifying Urban Sprawl With Spatial Autocorrelation Techniques Using Multi-Temporal Satellite Data

2019 ◽  
pp. 1624-1644
Author(s):  
Gabriele Nolè ◽  
Rosa Lasaponara ◽  
Antonio Lanorte ◽  
Beniamino Murgante
Keyword(s):  
Remote Sensing ◽  
Spatial Autocorrelation ◽  
Urban Areas ◽  
Urban Expansion ◽  
Data Sets ◽  
Temporal Data ◽  
Landsat Images ◽  
Urban Settlements ◽  
Multi Temporal ◽  
Soil Consumption

This study deals with the use of satellite TM multi-temporal data coupled with statistical analyses to quantitatively estimate urban expansion and soil consumption for small towns in southern Italy. The investigated area is close to Bari and was selected because highly representative for Italian urban areas. To cope with the fact that small changes have to be captured and extracted from TM multi-temporal data sets, we adopted the use of spectral indices to emphasize occurring changes, and geospatial data analysis to reveal spatial patterns. Analyses have been carried out using global and local spatial autocorrelation, applied to multi-date NASA Landsat images acquired in 1999 and 2009 and available free of charge. Moreover, in this paper each step of data processing has been carried out using free or open source software tools, such as, operating system (Linux Ubuntu), GIS software (GRASS GIS and Quantum GIS) and software for statistical analysis of data (R). This aspect is very important, since it puts no limits and allows everybody to carry out spatial analyses on remote sensing data. This approach can be very useful to assess and map land cover change and soil degradation, even for small urbanized areas, as in the case of Italy, where recently an increasing number of devastating flash floods have been recorded. These events have been mainly linked to urban expansion and soil consumption and have caused loss of human lives along with enormous damages to urban settlements, bridges, roads, agricultural activities, etc. In these cases, remote sensing can provide reliable operational low cost tools to assess, quantify and map risk areas.

Integration of Satellite Remote Sensing Techniques and Landscape Metrics to Characterize Land Cover Change and Dynamics

2013 ◽  
pp. 228-244
Author(s):  
Carmelo Riccardo Fichera ◽  
Giuseppe Modica ◽  
Maurizio Pollino
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Landscape Metrics ◽  
Landsat Images ◽  
Aerial Photos ◽  
Multi Temporal ◽  
Urban Rural ◽  
Remote Sensing Techniques

One of the most relevant applications of Remote Sensing (RS) techniques is related to the analysis and the characterization of Land Cover (LC) and its change, very useful to efficiently undertake land planning and management policies. Here, a case study is described, conducted in the area of Avellino (Southern Italy) by means of RS in combination with GIS and landscape metrics. A multi-temporal dataset of RS imagery has been used: aerial photos (1954, 1974, 1990), Landsat images (MSS 1975, TM 1985 and 1993, ETM+ 2004), and digital orthophotos (1994 and 2006). To characterize the dynamics of changes during a fifty year period (1954-2004), the approach has integrated temporal trend analysis and landscape metrics, focusing on the urban-rural gradient. Aerial photos and satellite images have been classified to obtain maps of LC changes, for fixed intervals: 1954-1985 and 1985-2004. LC pattern and its change are linked to both natural and social processes, whose driving role has been clearly demonstrated in the case analysed. In fact, after the disastrous Irpinia earthquake (1980), the local specific zoning laws and urban plans have significantly addressed landscape changes.

Quantifying Urban Expansion Using Landsat Images and Landscape Metrics: A Case Study of the Halton Region, Ontario

GEOMATICA ◽  
2020 ◽  
Author(s):  
Liyuan Qing ◽  
Hasti A. Petrosian ◽  
Sarah N. Fatholahi ◽  
Michael A. Chapman ◽  
Jonathan Li
Keyword(s):  
Landscape Metrics ◽  
Urban Areas ◽  
Census Data ◽  
Urban Expansion ◽  
Landsat Images ◽  
Factors Affecting ◽  
Supervised Learning Algorithms ◽  
Dramatic Rise ◽  
Multi Temporal ◽  
Gis Software

Urbanization is considered as one of the main factors affecting global change. The Halton Region as part of the Great Toronto Area (GTA), is regarded as one of the fastest growing regions in Canada, generating 20% of national GDP. It is also one of the most desirable places for living and thriving business. This research attempts to assess the urban expansion in the Halton Region, Ontario, Canada from 1989 to 2019 using satellite images, analysis approaches and landscape metrics. Multi-temporal Landsat images, and the supervised learning algorithms in GIS software were used to explore the dynamic changes, and to classify the urban and non-urban areas. The temporal urban expansion in the Halton Region experienced a dramatic rise, and mainly occurred from the centre of the area. The analysis of landscape metrics based on different methods, including Land Use in Central Indiana (LUCI) model, Vegetation-Impervious Surface-soil (V-I-S) model, and the census data of Canada was carried out to understand the transition mode of the urbanization in the Halton Region. Also, the population growth in the centre of the Halton Region was considered as one of driven forces affecting urban expansion. The results showed that most of the landscape metrics rose between 1989 and 2019, indicating leapfrog pattern of urbanization occurred over the entire period. The contribution of this research is to evaluate the urbanization in the Halton Region, and give the city managers a clear mind to make appropriate decisions in further urban planning.

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