Detection of Land Use/Land Cover Changes and Urban Sprawl in Al-Khobar, Saudi Arabia: An Analysis of Multi-Temporal Remote Sensing Data

Digital change detection is the process that helps in determining the changes associated with Land use and Land cover properties with reference to geo-referenced multi-temporal remote sensing data. It helps in identifying change between two or more dates that is uncharacterized of normal variation. This work is an attempt to assess the district-wise changes in land use/land cover in Delhi, India. The study made use of LISS -III imageries of 2008 and 2012 year. The images were classified using Maximum Likelihood classification method. The output can be useful in many applications such as Land use changes, habitat fragmentation, rate of deforestation, urban sprawl and other cumulative changes through spatial and temporal analysis. The study shows that Delhi land cover from 2008 to 2012 a major rapid changes in the landscape as there is high growth in the fallow and built up area. Agriculture land and forest area has reduced marginally and water body is showing almost stagnant condition over time.

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Land use, land cover change analysis with multitemporal remote sensing data

10.1117/12.698196 ◽

2006 ◽

Author(s):

K. Suzanchi ◽

R. N. Sahoo ◽

N. Kalra ◽

S. Pandey

Keyword(s):

Remote Sensing ◽

Land Use ◽

Land Cover ◽

Land Cover Change ◽

Remote Sensing Data ◽

Land Use Land Cover ◽

Change Analysis ◽

Sensing Data ◽

Multitemporal Remote Sensing ◽

Land Cover Change Analysis

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Estimating the Volume of Sediments and Assessing the Water Balance of the Badra Basin, Eastern Iraq, Using Swat Model and Remote Sensing Data

Iraqi Geological Journal ◽

10.46717/igj.54.2c.9ms-2021-09-28 ◽

2021 ◽

Vol 54 (2C) ◽

pp. 88-99

Author(s):

Awad A. Sahar

Keyword(s):

Remote Sensing ◽

Land Use ◽

Land Cover ◽

Water Balance ◽

Assessment Tool ◽

Remote Sensing Data ◽

Landsat 8 ◽

Land Use Land Cover ◽

Sensing Data ◽

Water Assessment

The primary objective of this study is to employ the remote sensing data and Soil & Water Assessment Tool model to estimate sediment volume and assess the water balance of the Badra Basin (2,615km2) in eastern Iraq. Remote sensing data was utilized as the main input with the Soil & Water Assessment Tool model. These data involved a land use-land cover map that was constructed by the classification of the Landsat-8 satellite imagery for the year 2020, STMR digital elevation model, soil map was acquired from the Food and Agriculture Organization and climatic data were sourced from the NASA-funded prediction of Worldwide Energy Resource The results discovered that about 40 % and 18% of the yearly rainfall are losing by evapotranspiration and filtration. The average amount of annual sediment transported was predicted at 120.47 tons /ha, 2018 recorded the highest value of transported sediment which is about 360 tons /ha. The volume of annual runoff was assessed at about 340.74 million m3. These results proved that the Soil & Water Assessment tool model has the ability to estimation the sediment and runoff volume. The climatic elements, especially rainfall, in addition to soil classes, topography, and land use-land cover had a significant impact on the amount of transported sediments and the volume of runoff.

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