Landsat-based vegetation abundance and surface temperature for surface urban heat island studies: the tale of Greater Amman Municipality

Jaber, Salahuddin M.

doi:10.1080/19475683.2018.1471519

Cited by 19 publications

(10 citation statements)

References 36 publications

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“…The global urban population represented 54.5% in 2016 (Rousta et al 2018). It is expected that 2/3 of the global population will be concentrated on urban areas by the mid-twentieth century (Jaber 2018;Hosseini, Pourahmad, and Pajoohan 2016). This means that 2/3 of the global population will not only experience increased temperature from global warming but also increased temperature due to Urban Heat Island (UHI) effects.…”

Section: Introductionmentioning

confidence: 99%

Impact of spatiotemporal land-use and land-cover changes on surface urban heat islands in a semiarid region using Landsat data

Maskooni

Hashemi

Berndtsson

et al. 2020

International Journal of Digital Earth

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Many factors are involved in urban heat island development, such as lack of green spaces, improper choice of building materials, densification, and other human activities. The aim of this research was to quantify the effects of land-use/land-cover (LU/LC) changes on urban land surface temperature (LST) during a 25-year period (1993-2018) for the semiarid Shiraz City in southern Iran using Landsat data (TM, ETM + , and OLI/TIRS) and machine learning algorithms. Five main LU/LC classes, such as orchard, vegetation, bare surface, asphalt cover, and built-up areas, were identified using a support vector machine algorithm. Landsat images were used to retrieve normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI). The results showed that the mean LST over the entire study domain increased considerably between 1993 and 2018, due to urbanization, decrease of green areas, and increasing industrial areas. Built-up areas increased considerably by 25.8% from 80 to 100.6 km 2 between 1993 and 2018, while vegetation cover decreased dramatically by 69.3%. Mean LST increased from 38.4 to 40.2°C during the 25-year period with a significant increase of 3.9°C between 2013 and 2018. In addition, the Urban heat island Ratio Index (URI) displayed a substantial upward trend during the 25-year period.

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Section: Introductionmentioning

confidence: 99%

Impact of spatiotemporal land-use and land-cover changes on surface urban heat islands in a semiarid region using Landsat data

Maskooni

Hashemi

Berndtsson

et al. 2020

International Journal of Digital Earth

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“…Only a few researchers have investigated Amman's urban growth and related LULC changes, such as (Al-Bilbisi, 2019;Jaber, 2018;Al-Kofahi et al, 2018), but no research has been conducted on how these changes affect the city's surface temperature and its environmental impacts. This study adds and extends previous work such as which conducted in Amman by Jaber, 2018.…”

Section: Study Contribution and Objectivesmentioning

confidence: 99%

Nvironmental Impacts of Urban Sprawl Using Remote Sensing Indices: A Case Study of Amman City – The Capital of Jordan

ABDELJAWAD¹,

ADEDOKUN²,

NAGY³

2023

GTG

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Urban sprawl is known to have a negative impact on the environment and is considered one of the main challenges in urban development. The current work aims to understand the spatio-temporal characteristics of urban growth and its environmental impact in the city of Amman. The paper's empirical method relies on NDVI, NDBI, and LST calculated using remote sensing and GIS techniques. For this study, Landsat Thematic Mapper Landsat 5-TM images from 1990, 1995, 1999, and 2004 were acquired, as well as Landsat 8-OLI images from 2013, 2017, and 2022. The surface temperature of Amman has increased as the area of paved roads, residential, commercial and industrial land use types have increased, while green spaces and vacant lots have decreased. The NDVI analysis revealed that there was variation in the vegetation index during the study year due to human activities as well as climatic change. Correlations between biophysical variables and LST revealed that NDVI had a significant negative correlation, while NDBI had a positive relationship. Further, a significant positive correlation was observed between road networks and built-up areas which enhance the occurrence of urban sprawl due to urbanization. This study highlighted the significance of considering the environmental consequences of urban sprawl when developing and implementing GAM policies and strategies.

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“…ML = Band-specific multiplicative rescaling factor, AL = Band-specific additive rescaling factor, dan Qcal = DN pada setiap piksel dalam band Landsat 8. Koreksi Radiometrik menggunakan persamaan sebagai berikut (Jaber, 2018;Suwarsono & Khomarudin, 2014;Kustandiyo et al, 2014):…”

Section: Gambar 1 Peta Lokasi Kajianunclassified

Hubungan Dual Polametric SAR Band – C dan Landsat 8 untuk Identifikasi Potensi Kekeringan

Assidiq

Rokhmana

2021

Geoid

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Kekeringan merupakan salah satu bencana krusial dan kompleks yang dapat menimbulkan kerugian material dan immaterial. Kekeringan di indonesia dikategorikan beberapa jenis meliputi Kekeringan Pertanian, Kekeringan Meterologis, dan kekeringan Hidrologi. Kekeringan pertanian merupakan kondisi dimana adanya penurunan kandungan air di dalam tanah. Kondisi tersebut akan berdampak pada tumbuhan dan atau tutupan lahan sehingga diperlukan tindakan preventif. Tindakan preventif dilakukan dengan cepat, efektif dan efisien sehingga pendekatan dengan pola dinamis sangat diperlukan. Pendekatan pola dinamis dilakukan dengan dengan metode yang dapat dilakukan setiap waktu. Penggunaan penginderaan jauh sensor aktif dapat menjadi solusi melalui pemantaaun setiap waktu secara dinamis. Salah satunya yaitu satelit dengan sensor radar, yaitu Sentinel 1. Sentinel 1A memiliki gelombang band C. Polarisasi pada citra Sentinel 1 memiliki bentuk dual-pol yang terdiri dari VV dan VH atau HH dan HV. Kombinasi polariasi memiliki potensi untuk digunakan identifikasi kekeringan. Metode yang dapat digunakan yaitu Radar Vegetation Index. Radar Vegetation Index dikembangkan dari algoritma NDVI. Klasifikasi kekeringan RVI dikembangkan dari analisis regresi hasil NDVI Landsat 8 dengan hasil RVI. Penelitian ini bertujuan untuk mengkaji pemanfaatan data SAR untuk identifikasi kekeringan dengan menghubungkan Dual Polametric SAR Band -C dan Landsat 8. Hasil penelitian bahwa NDVI memiliki Koefesien determinasi dengan RVI sebesar 0.2981.

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Landsat-based vegetation abundance and surface temperature for surface urban heat island studies: the tale of Greater Amman Municipality

Cited by 19 publications

References 36 publications

Impact of spatiotemporal land-use and land-cover changes on surface urban heat islands in a semiarid region using Landsat data

Impact of spatiotemporal land-use and land-cover changes on surface urban heat islands in a semiarid region using Landsat data

Nvironmental Impacts of Urban Sprawl Using Remote Sensing Indices: A Case Study of Amman City – The Capital of Jordan

Hubungan Dual Polametric SAR Band – C dan Landsat 8 untuk Identifikasi Potensi Kekeringan

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