Urban Land Cover Change Modelling Using Time-Series Satellite Images: A Case Study of Urban Growth in Five Cities of Saudi Arabia

Alqurashi, Abdullah F.; Kumar, Lalit; Sinha, Priyakant

doi:10.3390/rs8100838

Cited by 72 publications

(52 citation statements)

References 62 publications

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“…To use these samples, a process called signature extension as suggested by Foody [51,52] was undertaken as this allows us to use samples for unchanged areas for such purposes. In several studies in the recent past, this method has been used successfully [53][54][55]. To do this, additional processing was undertaken to include sample points that had not changed during the periods 1989-1977 and 1989-2000.…”

Section: Image Classificationmentioning

confidence: 99%

Mapping Long-Term Changes in Mangrove Species Composition and Distribution in the Sundarbans

Ghosh

Kumar

Roy

2016

Forests

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Abstract:The Sundarbans mangrove forest is an important resource for the people of the Ganges Delta. It plays an important role in the local as well as global ecosystem by absorbing carbon dioxide and other pollutants from air and water, offering protection to millions of people in the Ganges Delta against cyclone and water surges, stabilizing the shore line, trapping sediment and nutrients, purifying water, and providing services for human beings, such as fuel wood, medicine, food, and construction materials. However, this mangrove ecosystem is under threat, mainly due to climate change and anthropogenic factors. Anthropogenic and climate change-induced degradation, such as over-exploitation of timber and pollution, sea level rise, coastal erosion, increasing salinity, effects of increasing number of cyclones and higher levels of storm surges function as recurrent threats to mangroves in the Sundarbans. In this situation, regular and detailed information on mangrove species composition, their spatial distribution and the changes taking place over time is very important for a thorough understanding of mangrove biodiversity, and this information can also lead to the adoption of management practices designed for the maximum sustainable yield of the Sundarbans forest resources. We employed a maximum likelihood classifier technique to classify images recorded by the Landsat satellite series and used post classification comparison techniques to detect changes at the species level. The image classification resulted in overall accuracies of 72%, 83%, 79% and 89% for the images of 1977, 1989, 2000 and 2015, respectively. We identified five major mangrove species and detected changes over the 38-year (1977-2015) study period. During this period, both Heritiera fomes and Excoecaria agallocha decreased by 9.9%, while Ceriops decandra, Sonneratia apelatala, and Xylocarpus mekongensis increased by 12.9%, 380.4% and 57.3%, respectively.

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Section: Image Classificationmentioning

confidence: 99%

Mapping Long-Term Changes in Mangrove Species Composition and Distribution in the Sundarbans

Ghosh

Kumar

Roy

2016

Forests

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“…Urban expansion results in the change of urban land cover and the expansion of a city's border, which is necessary for accommodating a growing population and providing them with public city services [1]. Furthermore, it is important to continuously update land cover maps at macro-and micro-scales, which helps governments to be prepared for emergency monitoring of cities, especially after natural hazards [2][3][4][5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Texture Analysis and Land Cover Classification of Tehran Using Polarimetric Synthetic Aperture Radar Imagery

Zakeri¹,

Yamazaki²,

Li³

2017

Applied Sciences

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Abstract:Land cover classification of built-up and bare land areas in arid or semi-arid regions from multi-spectral optical images is not simple, due to the similarity of the spectral characteristics of the ground and building materials. However, synthetic aperture radar (SAR) images could overcome this issue because of the backscattering dependency on the material and the geometry of different surface objects. Therefore, in this paper, dual-polarized data from ALOS-2 PALSAR-2 (HH, HV) and Sentinel-1 C-SAR (VV, VH) were used to classify the land cover of Tehran city, Iran, which has grown rapidly in recent years. In addition, texture analysis was adopted to improve the land cover classification accuracy. In total, eight texture measures were calculated from SAR data. Then, principal component analysis was applied, and the first three components were selected for combination with the backscattering polarized images. Additionally, two supervised classification algorithms, support vector machine and maximum likelihood, were used to detect bare land, vegetation, and three different built-up classes. The results indicate that land cover classification obtained from backscatter values has better performance than that obtained from optical images. Furthermore, the layer stacking of texture features and backscatter values significantly increases the overall accuracy.

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“…We established LULC classes based on bi-spectral plots, ground survey data, and NDVI temporal profiles, and verified the results using high-resolution maps in Google Earth images for the same years. Alqurashi [11] used objectbased image analysis (OBIA) segmentation and classification for mapping LULC in five cities in Saudi Arabia and, depending on the size of patches of LULC, created two levels of image segmentation and classification by adjusting the thresholds in the rules applied. Similarly Cao [12] used an OBIA-based decision tree process to map 13 land-use types, with visual modification employed to improve the classification.…”

Section: Discusssion On Land-use/land-covermentioning

confidence: 99%

Urban Sprawl and Adverse Impacts on Agricultural Land: A Case Study on Hyderabad, India

et al. 2017

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Many Indian capitals are rapidly becoming megacities due to industrialization and rural-urban emigration. Land use within city boundaries has changed dynamically, accommodating development while replacing traditional land-use patterns. Using Landsat-8 and IRS-P6 data, this study investigated land-use changes in urban and peri-urban Hyderabad and their influence on land-use and land-cover. Advanced methods, such as spectral matching techniques with ground information were deployed in the analysis. From 2005 to 2016, the wastewater-irrigated area adjacent to the Musi river increased from 15,553 to 20,573 hectares, with concurrent expansion of the city boundaries from 38,863 to 80,111 hectares. Opportunistic shifts in land-use, especially related to wastewater-irrigated agriculture, emerged in response to growing demand for fresh vegetables and urban livestock feed, and to easy access to markets due to the city's expansion. Validation performed on the land-use maps developed revealed 80-85% accuracy.

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Urban Land Cover Change Modelling Using Time-Series Satellite Images: A Case Study of Urban Growth in Five Cities of Saudi Arabia

Cited by 72 publications

References 62 publications

Mapping Long-Term Changes in Mangrove Species Composition and Distribution in the Sundarbans

Mapping Long-Term Changes in Mangrove Species Composition and Distribution in the Sundarbans

Texture Analysis and Land Cover Classification of Tehran Using Polarimetric Synthetic Aperture Radar Imagery

Urban Sprawl and Adverse Impacts on Agricultural Land: A Case Study on Hyderabad, India

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