Comparasion of NDBI and NDVI as Indicators of Surface Urban Heat Island Effect in Landsat 8 Imagery: A Case Study of Iasi

Macarof, Paul; Stătescu, Florian

doi:10.1515/pesd-2017-0032

Cited by 72 publications

(37 citation statements)

References 15 publications

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“…Random forest classification accuracy depends on the number of trees and the number of random features used for classification, which are two user-defined parameters [28,31]. Among the features were NDVI (Normalized Difference Vegetation Index) [32] and MNDWI (Modified Normalized Difference Water Area Index) [33], as they might improve classification accuracy. The OOB (out-of-bag) test was used to estimate the test set accuracy [28,29].…”

Section: Random Forest Classifiermentioning

confidence: 99%

Land-Use Carbon Emissions Estimation for the Yangtze River Delta Urban Agglomeration Using 1994–2016 Landsat Image Data

Cui

Chen

et al. 2018

Remote Sensing

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The amount and growth rate of carbon emissions have been accelerated on a global scale since the industrial revolution (1800), especially in recent decades. This has resulted in a significant influence on the natural environment and human societies. Therefore, carbon emission reduction receives continuously increasing public attention and has long been under debate. In this study, we made use of the land-use specific carbon emission coefficients from previous studies and estimated the land-use carbon emissions and carbon intensities of the Yangtze River Delta Urban Agglomeration (YRDUA)—which consists of 26 eastern Chinese cities—from Landsat image data and socio-economic statistics in 1995, 2005, and 2015. In addition, spatial autocorrelation models including both global and local Moran’s I were used to analyze the spatial autocorrelation of carbon emissions and carbon intensities. It was found that (1) the YRDUA witnessed a rapidly increasing trend for net carbon emissions and a decreasing trend for carbon intensity over the two decades; (2) the spatial differences in carbon intensity had gradually narrowed, but were large in carbon emissions and had gradually increased; and (3) the carbon emissions in 2005 and 2015 had significant spatial autocorrelations. We concluded that (1) from 1995 to 2015 in the YRDUA, carbon emissions were large for the cities along the Yangtze River and carbon intensities were high for Anhui province’s resource-based cities, while both carbon emissions and carbon intensities were small for Zhejiang province’s cities; (2) over two decades, the increase in carbon emissions from urban land was approximately twice the increase in urban land area. Our study can provide useful insights into the assignment of carbon reduction tasks within the YRDUA.

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Section: Random Forest Classifiermentioning

confidence: 99%

Land-Use Carbon Emissions Estimation for the Yangtze River Delta Urban Agglomeration Using 1994–2016 Landsat Image Data

Cui

Chen

et al. 2018

Remote Sensing

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“…This indicator is also sensitive to cloudiness but research indicates that it can be used as a complementary indice of surface urban heat island effects to the traditionally applied NDVI. (Macarof and Statescu, 2017) The other advantage is 12day repeat cycle so data can be used for solving various environmental problems. The biggest challenge is to combine these resources in such a way that it be possible to get the most reliable information about impervious surfaces in urban spaces.…”

Section: Introductionmentioning

confidence: 99%

Sentinel-2 Imagery for Mapping and Monitoring Imperviousness in Urban Areas

Kuc

Chormański

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> Nowadays there is need to take the action to find out the innovative solutions to reduce the negative effects of climate change in urban areas. Cities face multiple challenges in water management and flood protection at local scale, especially given an uncertain future climate and a rapidly growing population. The density of impervious surfaces (roofs and pavement) is a key element in evaluating hydrologic processes and occurring the surface runoff – the major component of water cycle in urban catchment. Satellite imagery are powerful and up-to-date data for estimation human impact on urban space. It is also highlighted the possibility of using those results in urban development studies according to adaptation cities to climate change. The Copernicus SENTINEL constellation brings out the potential to provide high quality and free of charge satellites images that enable to estimate parameters related to urban structure and imperviousness. Multitemporal Sentinel-2 data are widely used to calculate the indices for urban sprawl. The normalized difference built-up index (NDBI) and normalized difference vegetation index (NDVI), indicators providing information on urban heat Island effect, are useful for change detection of imperviousness percentage in urban areas. The main objective of this research is fusion of multi-temporal and multi-resolution SENTINEL-2 MSI data for developing a new, innovative approach for improving water management in urban catchments at local scale provide valuable input data for hydrological model. The results obtained were compared by quantitative comparison with Imperviousness HRL of Copernicus Program, visual comparison with OpenStreetMap data as well as qualitative comparison with the high resolution Planet’s.</p>

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“…The Normalized Difference Vegetation Index (NDVI) is an algorithm that is applied to multi-channel imagery to identify vegetation density [3]. NDVI is the best known and often used vegetation index.…”

Section: Introductionmentioning

confidence: 99%

Impact Of NDVI Change To Spatial Distribution Of Land Surface Temperature (A Study in Medan City, Indonesia)

Rahmad¹,

Nurman²,

Pinem³

2019

Proceedings of the 1st International Conference on Social Sciences and Interdisciplinary Studies (ICSSIS 2018)

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This study aims to determine: (1) spatialtemporal variations in land surface temperature in Medan City, (2) spatial-temporal variations in vegetation index values (NDVI) in Medan City, (3) the relationship between LST and NDVI in Medan City. The design of this research is correlational and population research conducted with a spatial and temporal approach to analyse data spatially in different years of image recording. The variables in this study are land surface temperature and vegetation density. Data collection is done by image interpretation, observation, and documentation methods-data analysis techniques in image interpretation in the form of image transformation LST, NDVI, and spatial statistics. The results showed that the lowest land surface temperature of Medan City in 2011 was 20.72 o C and the highest was 31.82 o C with an average temperature of 26.05 o C.The lowest temperature found in Medan Labuhan, and the highest temperature found in Medan Denai. For 2018 shows the lowest temperature is 19.32 o C in Medan Belawan Sub-district, while the highest temperature is 34.43 o C in Medan Denai Sub-district. The average temperature for 2018 is 28.1 o C. Accuracy test shows there is a strong relationship between LST and temperature measurement. NDVI transformation results show that in 2011 the vegetation density of Medan City was dominated by medium density classes and in 2018 dominated by lowdensity class. This result shows a decrease in the level of vegetation density in Medan City.

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Comparasion of NDBI and NDVI as Indicators of Surface Urban Heat Island Effect in Landsat 8 Imagery: A Case Study of Iasi

Cited by 72 publications

References 15 publications

Land-Use Carbon Emissions Estimation for the Yangtze River Delta Urban Agglomeration Using 1994–2016 Landsat Image Data

Land-Use Carbon Emissions Estimation for the Yangtze River Delta Urban Agglomeration Using 1994–2016 Landsat Image Data

Sentinel-2 Imagery for Mapping and Monitoring Imperviousness in Urban Areas

Impact Of NDVI Change To Spatial Distribution Of Land Surface Temperature (A Study in Medan City, Indonesia)

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