Spectral index utility for summer urban heating analysis

Abstract

The land surface temperature (LST) retrieval in an urban environment by thermal remote sensing is a widespread scientific topic and several studies have been made to point out the correspondence between spectral indices and LST pattern. This work evaluates the potential of the spectral indices introduced in the literature, more than 30, by assessing their correlation with the summer LST in the heterogeneous urban area of Rome, Italy, considering two different triplets of images acquired during 2009 and 2011 by the Landsat Thematic Mapper (TM). The spectral indices have been divided into vegetation and built up-soil indices employing the reflective TM bands, and then ranked on the basis of their linear and monotonic relationship with the LST. Vegetation indices have a strong negative correlation with LST: vegetation area (VA), nonlinear index, modified soil adjusted vegetation index exhibit a greater Pearson and Spearman correlation coefficient with LST. The more useful spectral indices for built up and soil analysis, exhibiting a greater positive correlation with LST, are the impervious surface area (ISA), the bare soil index, the index-based built-up index, and the normalized difference built-up index.Interesting indications of the impact on the spectral index performance of specific land-cover classes embedded in an urban environment, such as the bare soil and the water classes were pointed out; for example, the reduction of the ISA and VA capability to display the full dynamic range of the LST pattern.

Contribution Degree of Different Surface Factors in Urban Interior to Urban Thermal Environment

“…is is also consistent with some previous studies and is very similar to the proportion composition in LULC cover LCLU [34,35] and the proportional composition of landscape (PLAND) [11,21,24]. Unlike in the past, when the thermal environment was explored by establishing various indices [16][17][18][19][20][21][22][23]41], we directly used the surface factor objects' thermal characteristics in the present study. After the establishment of the indexes, the same index value can contain different types of surface factors.…”

Monitoring LST-NDVI Relationship Using Premonsoon Landsat Datasets

“…Many previous research works applied various remote sensing indices to extract various land use classes [8,18,23,24,33,34,49,56].…”

“…A number of research scholars recently attempted to build the LST-NDVI correlation [12,[15][16][17]. Some previous attempts were spatiotemporal in nature and were mainly conducted on the big cities like Tokyo, Shanghai, Chongqing, Shijiazhuang, Rome, Shiraz, Melbourne, Bangkok, Monte Hermoso, Beijing, Islamabad [18][19][20][21][22][23][24][25][26][27], etc.…”

Downscaling Landsat Land Surface Temperature over the urban area of Florence

“…Multiple regressions comprising powers of predictors (a nth order polynomial regression) were also considered [Bonafoni, 2016], inserting the powers in a step-wise mode. Moreover other vegetation indices instead of NDVI [Bonafoni, 2015] were evaluated, but the results were quite similar. The lower root mean square error (RMSE) between estimates and aircraft LST was obtained by the model: …”