Analyzing the interpretative ability of landscape pattern to explain thermal environmental effects in the Beijing-Tianjin-Hebei urban agglomeration.

Dongchuan Wang,Wei Zhang,Xian Zhang,Zhichao Sun,Xiao Wang,Junhe Chen

doi:10.7717/peerj.7874

Abstract

The development of the urban agglomeration has caused drastic changes in landscape pattern and increased anthropogenic heat emission and lead to the urban heat island (UHI) effect more serious. Therefore, understanding the interpretation ability of landscape pattern on the thermal environment has gradually become an important focus. In the study, the spatial heterogeneity of the surface temperature was analyzed using the hot-spot analysis method which was improved by changing the calculation of space weight. Then the interpretation ability of a single landscape and a combination of landscapes to explain surface temperature was explored using the Pearson correlation coefficient and ordinary least squares regression from different spatial levels, and the spatial heterogeneity of the interpretation ability was explored using geographical weighted regression under the optimal granularity (5 × 5 km). The results showed that: (1) The hot spots of surface temperature were distributed mainly in the plains and on the southeast hills, where the landscapes primarily include artificial landscape (ArtLS) and farmland landscape (FarmLS). The cold spots were distributed mainly in the northern hills, which are dominated by forest landscape (ForLS). (2) On the whole, the interpretative ability of ForLS, FarmLS, ArtLS, green space landscape pattern, and ecological landscape pattern to explain surface temperature was stronger, whereas the interpretative ability of grassland landscape and wetland landscape to explain surface temperature was weaker. The interpretation ability of landscape pattern to explain surface temperature was obviously different in different areas. Specifically, the ability was stronger in the hills than in the plain and plateau. The results are intended to provide a scientific basis for adjusting landscape structural, optimizing landscape patterns, alleviating the UHI effect, and coordinating the balance among cities within the urban agglomeration.

Highlights

In recent years, because of the rapid development of urbanization, artificial landscape (ArtLS) has gradually eroded the natural landscape (Su et al, 2012; Angel et al, 2011; Miao et al, 2011)
Since the change of landscape pattern is related to human activities (Li et al, 2016), we divided the landscapes of the Beijing-Tianjin-Hebei urban agglomeration into six landscapes according to the degree of human influence on the landscape shaping process and primary classification results of land cover: (1) forest landscape (ForLS), mainly referring to the forest land in the land cover classification; (2) grassland landscape (GraLS), mainly referring to grassland in the land cover classification; (3) wetland landscape (WetLS), mainly referring to rivers, lakes, and other water areas in the land cover classification; (4) farmland landscape (FarmLS), mainly referring to cultivated land in land cover classification; (5) ArtLS, mainly referring to the artificial surface in land cover classification; (6) bare land landscape, mainly referring to the unused land in the land cover classification
Interpretative ability of landscape pattern to explain surface temperature Interpretative ability of landscape pattern to explain surface temperature in the study area According to Table 4, we found that the interpretative ability of ForLS, FarmLS, ArtLS, GreenLSP, and EcoLSP to explain surface temperature was strong, whereas the

Summary

Introduction

Because of the rapid development of urbanization, artificial landscape (ArtLS) has gradually eroded the natural landscape (Su et al, 2012; Angel et al, 2011; Miao et al, 2011). Impermeable layers with high thermal conductivity which are composed of cement and asphalt continue to expand, and energy and material consumption keep increasing which lead to greenhouse gas and anthropogenic heat emissions increasing (Bonafoni et al, 2017; Barrington-Leigh & Millard-Ball, 2015). This has changed the heat flow and heat balance between atmosphere and land surface, aggravated the heat island effect, and has seriously threatened the urban living environment and the quality of life for dwellers (Zhang et al, 2009; Zhijia et al, 2016). The study on the coupling relationship between landscape pattern and surface temperature is the breakthrough to alleviate the UHI effect (Li et al, 2011, 2017)

Methods

Results

Discussion

Conclusion