Assessing the effects of land use and land cover patterns on thermal conditions using landscape metrics in city of Indianapolis, United States

Abstract

Direct applications of remote sensing thermal infrared (TIR) data in landscape ecological research are rare due to limitations in the sensors, calibration, and difficulty in interpretation. Currently there is a general lack of methodology for examining the relationship between land surface temperatures (LST) derived from TIR data and landscape patterns extracted from optical sensors. A separation of landscapes into values directly related to their scale and signature is a key step. In this study, a Landsat ETM+ image of Indianapolis, Unites States, acquired on June 22, 2000, was spectrally unmixed (using spectral mixture analysis, SMA) into fraction endmembers of green vegetation, soil, high albedo, and low albedo. Impervious surface was then computed from the high and low albedo images. A hybrid classification procedure was developed to classify the fraction images into seven land use and land cover (LULC) classes. Using the fractional images, the landscape composition and pattern were examined. Next, pixel-based LST measurements were correlated with the landscape fractional components to investigate LULC based relationships between LST and impervious surface and green vegetation fractions. An examination of the relationship between the LULC and LST maps with landscape metrics was finally conducted to deepen understanding of their interactions. Results indicate that SMA-derived fraction images were effective for quantifying the urban morphology and for providing reliable measurements of biophysical variables. LST was found to be positively correlated with impervious surface fraction but negatively correlated with green vegetation fraction. Each temperature zone was associated with a dominant LULC category. Further research should be directed to the theoretical and applied implications of describing such relationships between LULC patterns and urban thermal conditions.

Application of a new integrated landscape index to predict potential urban heat islands

“…For this purpose, a large body of research (Chen et al, 2014;Connors et al, 2013;Li et al, 2011;Weng et al, 2007;Zhou et al, 2011) has focused on the relationship between surface UHIs and landscape patterns using landscape indices based on the concept of landscape ecology; that is, the theory that landscape patterns strongly affect and are affected by ecological processes (Turner, 1989). Composition and configuration, as two aspects of landscape patterns, were both tested (Chen et al, 2014;Connors et al, 2013;Zhou et al, 2011), with the conclusion that composition is more effective than configuration in influencing LSTs or surface urban heat islands (SUHIs) (Chen et al, 2014;Li et al, 2012;Zhou et al, 2011).…”

How many metrics are required to identify the effects of the landscape pattern on land surface temperature?

“…The software FRAGSTATS developed by McGarigal et al (2012) has became widely used for calculating landscape metrics in many fields, ranging from evaluating land use/cover changes to analyze the effects of landscape pattern on different kinds of ecosystem services (Uuemaa et al, 2013). However, the number of landscape metrics used in previous studies has ranged from 2 to more than 10 (Cao et al, 2010;Connors et al, 2013;Li et al, 2012Li et al, , 2013Sun et al, 2012;Weng et al, 2007). Frequently used metrics have included: percentage of landscape (PLAND), mean patch size (MPS), perimeter-area fractal dimension (PAFRAC), aggregation index (AI), cohesion index (COHESION), contagion index (CONTAG), and Shannon's diversity index (SHDI).…”

Comparative Analysis of Responses of Land Surface Temperature to Long-Term Land Use/Cover Changes between a Coastal and Inland City: A Case of Freetown and Bo Town in Sierra Leone

“…Unlike in Freetown, the growth of Bo town has led to a reduction in the area of the densely vegetated LULC category between 1985 and 2015. As observed in Bo town, in most studies, the proportion of total area occupied by dense vegetation decreases with continuous urban expansion [26,27,53,54]. Kamusoko et al [54] observed that the expansion of built-up areas in Harare Zimbabwe pushed most dense vegetation locations outwards to the peripheries of the city.…”

“…The steps as summarised by Weng et al [26] and described in detail by Weng et al [44] are followed to retrieve the land surface temperature from Landsat's thermal infrared data. The procedure involved (i) conversion of digital numbers (DN) to spectral radiance; (ii) computation of satellite brightness temperature from spectral radiance; and (iii) retrieval of land surface temperature from brightness temperature (emissivity correction).…”