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

Abstract

a b s t r a c tStudies on the interactions between urban landscape patterns and land surface temperatures are the key to urban heat island (UHI) mitigation. However, the landscape pattern indices used in previous studies were inconsistent in type and number. Furthermore, few studies combined the composition and configuration indices into one integrated indicator. The description of landscape pattern is thus a great yet rewarding challenge. In this study, we used the integrated location weighted landscape index (LWLI) proposed by Chen et al. (2009) and revised it to indicate potential UHIs. Fifty-six circular landscape samples along four transects were created and the LWLI was derived based on the theory of the GINI index. The LWLI considers the type, composition and configuration of different land covers inside a landscape, where configuration is treated as location weights. The potential UHI was represented by the land surface temperature (LST) of each circle landscape center. The correlation analysis results showed that the LWLI was significantly positively correlated with summer potential UHIs, with a Pearson R equaling 0.736, and barely correlated with winter potential UHI. Moreover, the LWLI was slightly more strongly correlated with potential UHI than composition alone. These correlations weakened as the landscapes grew larger. The linear regression results further revealed that the LWLI explained about 53% of the summer potential UHI, which was slightly better than composition alone (49%). This indicated that the LWLI was as effective in predicting the potential UHI as the combination of several pattern indicators, echoing Tobler's first law of geography, which states that "all things are related, but nearby things are more related than distant things." This study also leaves room for improvement of the index by integrating more environmental/ecological parameters as weights, and for further application of the index in other fields.

The Spatial Patterns of Land Surface Temperature and Its Impact Factors: Spatial Non-Stationarity and Scale Effects Based on a Geographically-Weighted Regression Model

“…One of the most prominent phenomena is the increasingly significant urban heat island (UHI) effect. The UHI effect refers to the phenomenon that the urban areas have higher temperatures than their surrounding suburban areas [3][4][5], which has attracted wide concern because of its adverse effect on human living environment, air quality and resident health [6][7][8][9].…”

“…In general, the UHI described in the term of air temperatures by the direct in-situ measurement and surface temperature from thermal remote sensing data [4,10]. With the development of remote sensing technology, satellite-based estimation of Land Surface Temperature (LST) and other surface temperature (e.g., lake surface temperatures, etc.)…”

Detecting Urban Landscape Factors Controlling Seasonal Land Surface Temperatures: From the Perspective of Urban Function Zones

“…A cluster analysis was firstly conducted to filter the most appropriate factors, in order to ensure the non-redundancy and representativeness of these factors ( Fig. 4) (Chen et al 2016). Then, random forest recursion feature elimination (RF-RFE) method is a sequence backward selection algorithm based on the principle of maximum interval, which was used to rank these metrics according their relative importance (Table 3), and eliminate the metrics with weak importance (Qi et al 2018).…”

“…Landscape metrics, referring to the theory of landscape ecology, are often used to quantify the spatial-temporal changes in landscape composition and configuration when describing urban thermal complexities (Chen et al 2012). However, traditional landscape metrics are not always optimal for analyzing landscape patterns due to metric redundancy (Chen et al 2016) and the lack of clear ecological analogues (Kedron et al 2018). Additional indices thus have been introduced into the UHI related studies.…”

Application of Source-Sink Landscape Influence Values to Commuter Traffic: A Case Study of Xiamen Island

“…Fifty-six kinds of landscape index can be calculated in FRAGSTATS 3.3 [11]. Many studies therefore use a set of indices rather than a comprehensive index to describe landscape patterns [12]. Source-sink landscape theory is recommended as an effective method for integrative analysis of landscape patterns and ecological processes [4].…”