Land cover classification and change detection analysis of Qaroun and Wadi El-Rayyan lakes using multi-temporal remotely sensed imagery

Mohamed, S. M.; El-Raey, M.

doi:10.1007/s10661-019-7339-x

Cited by 24 publications

(12 citation statements)

References 32 publications

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“…In 1906, Andrey Andreyevich Markov a Russian mathematician developed a technique known as Markov chain that gives new direction to predictive probability theory (Seneta 1996 ; Mohamed and El-Raey 2019 ). Markov chain is a random process used to model the change over a specified time period from one state to another, i.e.…”

Section: Background Model Descriptionmentioning

confidence: 99%

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Monitoring of land use land cover dynamics and prediction of urban growth using Land Change Modeler in Delhi and its environs, India

Singh

Venkatramanan

Deshmukh

2022

Environ Sci Pollut Res

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In the recent decades, cities have been expanding at a great pace which changes the landscape rapidly as a result of inflow of people from rural areas and economic progression. Therefore, understanding spatiotemporal dynamics of human induced land use land cover changes has become an important issue to deal with the challenges for making sustainable cities. This study aims to determine the rate of landscape transformations along with its causes and consequences as well as predicting urban growth pattern in Delhi and its environs. Landsat satellite images of 1989, 2000, 2010 and 2020 were used to determine the changes in land use land cover using supervised maximum likelihood classification. Subsequently, Land Change Modeler (LCM) module of TerrSet software was used to generate future urban growth for the year 2030 based on 2010 and 2020 dataset. Validation was carried out by overlaying the actual and simulated 2020 maps. The change detection results showed that urban and open areas increased by 13.44% and 2.40%, respectively, with a substantial decrease in crop land (10.88%) from 1989 to 2020 and forest area increased by 3.48% in 2020 due to restoration programmes. Furthermore, the simulated output of 2030 predicted an increase of 24.30% in urban area and kappa coefficient 0.96. Thus, knowledge of the present and predicted changes will help decision-makers and planners during the process of formulating new sustainable policies, master plans and economic strategies for rapidly growing cities with urban blue-green infrastructures.

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Section: Background Model Descriptionmentioning

confidence: 99%

“…Markov chain is a random process used to model the change over a specified time period from one state to another, i.e. from one LULC class to another and is expressed as (Gidey et al 2017 ; Mohamed and El-Raey 2019 ) where i = state, n = time.…”

Section: Background Model Descriptionmentioning

confidence: 99%

Monitoring of land use land cover dynamics and prediction of urban growth using Land Change Modeler in Delhi and its environs, India

Singh

Venkatramanan

Deshmukh

2022

Environ Sci Pollut Res

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“…Rainfall was considered the most significant parameter in alignment with literature (Radwan et al, 2018). Stream order and drainage density were considered as the second significant parameters, as flooded areas are regularly located near the rivers and high drainage density (Mohamed and El-Raey, 2019;Seejata et al, 2018). Elevation and distance from drainage were, therefore, assigned as moderate strongly significant; the areas located in low elevation and near the streams are frequently located in flooded areas (Mishra and Flash flood hazard mapping Sinha, 2020;Mohamed, 2019).…”

Section: Sensitivity Score (S F )mentioning

confidence: 99%

Flash flood hazard mapping based on AHP with GIS and satellite information in Kampong Speu Province, Cambodia

Koem

Tantanee

2020

IJDRBE

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Purpose Cambodia is considered one of the countries that are most vulnerable to adverse effects of climate change, particularly floods and droughts. Kampong Speu Province is a frequent site of calamitous flash floods. Reliable sources of flash flood information and analysis are critical in efforts to minimize the impact of flooding. Unfortunately, Cambodia does not yet have a comprehensive program for flash flood hazard mapping, with many places such as Kampong Speu Province having no such information resources available. The purpose of this paper is, therefore, to determine flash flood hazard levels across all of Kampong Speu Province using analytical hierarchy process (AHP) and geographical information system (GIS) with satellite information. Design/methodology/approach The integrated AHP–GIS analysis in this study encompasses ten parameters in the assessment of flash flood hazard levels across the province: rainfall, geology, soil, elevation, slope, stream order, flow direction, distance from drainage, drainage density and land use. The study uses a 10 × 10 pairwise matrix in AHP to compare the relative importance of each parameter and find each parameter’s weight. Finally, a flash flood hazard map is developed displaying all areas of Kampong Speu Province classified into five levels, with Level 5 being the most hazardous. Findings This study reveals that high and very high flash flood hazard levels are identified in the northwest part of Kampong Speu Province, particularly in Aoral, Phnum Srouch and Thpong districts and along Prek Thnot River and streams. Originality/value The flash flood hazard map developed here provides a wealth of information that can be invaluable for implementing effective disaster mitigation, improving disaster preparedness and optimizing land use.

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“…Land cover has the most delicate relation with land use [12,13]. Mapping land use-land cover (LULC) has been completed efficiently with satellite images at many spatial, spectral, and temporal resolutions [1,14,15], while in arid and semi-arid ecology, the application of multi-temporal satellite images has been reduced to develop and assess LULC fluctuations [16,17]. Alterations in LULC could be examined because they affect land ecology permanently, particularly in built-up areas of micro-climate heating [18].…”

Section: Introductionmentioning

confidence: 99%

Monitoring of Land Use–Land Cover Change and Potential Causal Factors of Climate Change in Jhelum District, Punjab, Pakistan, through GIS and Multi-Temporal Satellite Data

Majeed

Tariq

Anwar

et al. 2021

Land

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Land use–land cover (LULC) alteration is primarily associated with land degradation, especially in recent decades, and has resulted in various harmful changes in the landscape. The normalized difference vegetation index (NDVI) has the prospective capacity to classify the vegetative characteristics of many ecological areas and has proven itself useful as a remote sensing (RS) tool in recording vegetative phenological aspects. Likewise, the normalized difference built-up index (NDBI) is used for quoting built-up areas. The current research objectives include identification of LULC, NDVI, and NDBI changes in Jhelum District, Punjab, Pakistan, during the last 30 years (1990–2020). This study targeted five major LULC classes: water channels, built-up area, barren land, forest, and cultivated land. Satellite imagery classification tools were used to identify LULC changes in Jhelum District, northern Punjab, Pakistan. The perception data about the environmental variations as conveyed by the 500 participants (mainly farmers) were also recorded and analyzed. The results depict that the majority of farmers (54%) believe in the appearance of more drastic changes such as less rainfall, drought, and decreased water availability for irrigation during 2020 compared to 30 years prior. Overall accuracy assessment of imagery classification was 83.2% and 88.8% for 1990, 88.1% and 85.7% for 2000, 86.5% and 86.7% for 2010, and 85.6% and 87.3% for 2020. The NDVI for Jhelum District was the highest in 1990 at +0.86 and the lowest in 2020 at +0.32; similarly, NDBI values were the highest in 2020 at +0.72 and the lowest in 1990 at −0.36. LULC change showed a clear association with temperature, NDBI, and NDVI in the study area. At the same time, variations in the land area of barren soil, vegetation, and built-up from 1990 to 2020 were quite prominent, possibly resulting in temperature increases, reduction in water for irrigation, and changing rainfall patterns. Farmers were found to be quite responsive to such climatic variations, diverting to framing possible mitigation approaches, but they need government assistance. The findings of this study, especially the causes and impacts of rapid LULC variations in the study area, need immediate attention from related government departments and policy makers.

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Land cover classification and change detection analysis of Qaroun and Wadi El-Rayyan lakes using multi-temporal remotely sensed imagery

Cited by 24 publications

References 32 publications

Monitoring of land use land cover dynamics and prediction of urban growth using Land Change Modeler in Delhi and its environs, India

Monitoring of land use land cover dynamics and prediction of urban growth using Land Change Modeler in Delhi and its environs, India

Flash flood hazard mapping based on AHP with GIS and satellite information in Kampong Speu Province, Cambodia

Monitoring of Land Use–Land Cover Change and Potential Causal Factors of Climate Change in Jhelum District, Punjab, Pakistan, through GIS and Multi-Temporal Satellite Data

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