Retrieval of land surface temperature, normalized difference moisture index, normalized difference water index of the Ravi basin using Landsat data

Taloor, Ajay Kumar; Manhas, Drinder Singh; Kothyari, Girish Chandra

doi:10.1016/j.acags.2020.100051

Cited by 120 publications

(36 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The Landsat 8 satellite was successfully launched on 11 February 2013 and deployed into orbit with two instruments onboard (Valizadeh Kamran et al, 2015): the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS), which jointly produce a multispectral image consisting of 11 spectral bands. OLI collects nine spectral bands including a pan band: bands 1-4 correspond to the visible part of the electromagnetic spectrum, bands 5-7 and 9 to near-infrared and shortwave infrared regions of the spectrum (Taloor et al, 2021) (all of these have a spatial resolution of 30 m) (Jeevalakshmi et al, 2017); band 8 is panchromatic, taking images in the wide visible wavelength range (0,503-0,676 μm) with 15 m spatial resolution. These bands allow obtaining various kinds of information on the characteristics of the land surface, including vegetation cover.…”

Section: Methodsmentioning

confidence: 99%

Mapping summer SUHI and its impact on the environment using GIS and Remote Sensing techniques: A case study on Municipality of Prishtina (Kosovo)

Berila¹,

Isufi²

2021

EJG

View full text Add to dashboard Cite

Urban areas, compared to peripheral and rural areas, have higher temperatures which are caused by a series of unplanned activities that are undertaken by humans. Such a thing leads to the emergence of the Surface Urban Heat Island (SUHI) phenomenon. In this paper, summer SUHI is determined through the calculation of LST for the Municipality of Prishtina using GIS and Remote Sensing techniques. To make this calculation, the Landsat 8 satellite image with 0% cloud cover was used. From the calculations made it turns out that the pixels with the highest value of LST are found in those parts where the urban area appears, where there are numerous constructions with impermeable materials, as well as in those areas where there are bare surfaces. Whereas, the pixels with lower values of LST appear in those parts where there are vegetation and water bodies, making these areas fresher. The SUHI phenomenon makes the lives of citizens difficult, therefore, such information is very important for the leaders and urban planners of the city of Prishtina, so that they take a series of steps towards minimizing such an effect in order to the life of the citizens to be as healthy as possible.

show abstract

Section: Methodsmentioning

confidence: 99%

Mapping summer SUHI and its impact on the environment using GIS and Remote Sensing techniques: A case study on Municipality of Prishtina (Kosovo)

Berila¹,

Isufi²

2021

EJG

View full text Add to dashboard Cite

show abstract

“…It consists of two sensors OLI and Thermal Infrared Sensors (TIRs)in which OLI comprises of 8 bands located in the visible, near infrared and the short-wave infrared region of the spectrum which provides the data of 30 m spatial resolution. The TIRs senses the Thermal Infrared (TIR) radiance at a spatial resolution of 100 m with the help of two bands located in the atmospheric window between 10 and 12 μm [37]. For pre-processing the imagery, the species, through integration and interpretation of various habitat variables of both spatial and non-spatial nature [23].…”

Section: Estimation Of Land Surface Temperature (Lst)mentioning

confidence: 99%

Assessment of Wildlife Habitats Using Geo-Spatial Techniques; Implications for Long-Term Habitat Management of Girnar Wildlife Sanctuary, Gujarat, India

Aditya¹

2021

J Environ Stud

View full text Add to dashboard Cite

“…Google Earth Engine (GEE) (accessible at: https://code.earthengine.google.com/ accessed on 4 January 2021) was used to compute four indicators, namely normalized difference moisture index (NDMI), normalized difference water index (NDWI), normalized difference built-up index (NDBI), and normalized difference vegetation index (NDVI) from Landsat 8 OLI dataset. These four indices are well-defined and reliable in the field of RS [75][76][77][78]. GEE code editor was used to fetch the Landsat 8 TOA collection in the GEE environment "LANDSAT/LC08/C01/T1_TOA/".…”

Section: Data Processingmentioning

confidence: 99%

Investigating Eco-Environmental Vulnerability for China–Pakistan Economic Corridor Key Sector Punjab Using Multi-Sources Geo-Information

Kamran

Bian

et al. 2021

IJGI

View full text Add to dashboard Cite

China-Pakistan economic corridor (CPEC), a critical part of the Belt and Road initiative (BRI), is subjected to rapid infrastructure development, which may lead to potential eco-environmental vulnerability. This study uses multi-source geo-information, and the multi-criteria decision-making (MCDM)-based best–worst method (BWM) to quantify the baseline eco-environmental vulnerability of one key CPEC sector—the Punjab province. The Punjab province is an important connection between northern and southern CPEC routes in Pakistan. In this study, we have established an indicator system consisting of twenty-two influential factors in a geospatial database to conduct eco-environmental vulnerability analysis. The overall setup is supported by a geographic information system (GIS) to perform spatial analysis. The resulting map was categorized into five vulnerability levels: very low, low, medium, high, and very high. The results revealed that the overall eco-environmental health of the Punjab province is reasonably good as 4.64% and 59.45% area of the key sector lies in ‘very low’ and ‘low’ vulnerability categories; however, there also exist highly vulnerable areas, particularly in the proximity of CPEC projects. Although high vulnerability areas constitute a very small percentage, only 0.08% of the Punjab province, still, decision-makers need to be aware of those regions and make corresponding protection strategies. Our study demonstrated that the MCDM-BWM-based EVA model could be effectively used to quantify vulnerability in other areas of CPEC. The findings of the study emphasize that management policies should be aligned with research-based recommendations for ecological protection, natural resource utilization, and sustainable development in regions participating in Belt and Road Initiative (BRI).

show abstract

Retrieval of land surface temperature, normalized difference moisture index, normalized difference water index of the Ravi basin using Landsat data

Cited by 120 publications

References 44 publications

Mapping summer SUHI and its impact on the environment using GIS and Remote Sensing techniques: A case study on Municipality of Prishtina (Kosovo)

Mapping summer SUHI and its impact on the environment using GIS and Remote Sensing techniques: A case study on Municipality of Prishtina (Kosovo)

Assessment of Wildlife Habitats Using Geo-Spatial Techniques; Implications for Long-Term Habitat Management of Girnar Wildlife Sanctuary, Gujarat, India

Investigating Eco-Environmental Vulnerability for China–Pakistan Economic Corridor Key Sector Punjab Using Multi-Sources Geo-Information

Contact Info

Product

Resources

About