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Quantifying how landscape composition and configuration affect urban land surface temperatures using machine learning and neutral landscapes

Quantifying how landscape composition and configuration affect urban land surface temperatures using machine learning and neutral landscapes
Quantifying how landscape composition and configuration affect urban land surface temperatures using machine learning and neutral landscapes

The urban heat island effect is an important 21st century issue because it intersects with the complex challenges of urban population growth, global climate change, public health and increasing energy demand for cooling. While the effects of urban landscape composition on land surface temperature (LST) are well-studied, less attention has been paid to the spatial arrangement of land cover types especially in smaller, often more diverse cities. Landscape configuration is important because it offers the potential to provide refuge from excessive heat for both people and buildings. We present a novel approach to quantifying how both composition and configuration affect LST derived from Landsat imagery in Southampton, UK. First, we trained a machine-learning (generalized boosted regression) model to predict LST from landscape covariates that included the characteristics of the immediate pixel and its surroundings. The model achieved a correlation between predicted and measured LST of 0.956 on independent test data (n = 102,935) and included predictors for both the immediate and adjacent land use. In contrast to other studies, we found adjacency effects to be stronger than immediate effects at 30 m resolution. Next, we used a landscape generation tool (Landscape Generator) to alter landscape configuration by varying natural and built patch sizes and arrangements while holding composition constant. The generated neutral landscapes were then fed into the machine learning model to predict patterns of LST. When we manipulated landscape configuration, the average city temperature remained the same but the local minima varied by 0.9 °C and the maxima by 4.2 °C. The effects on LST and heat island metrics correlated with landscape fragmentation indices. Moreover, the surface temperature of buildings could be reduced by up to 2.1 °C through landscape manipulation. We found that the optimum mix of land use types is neither at the land-sharing nor land-sparing extremes, but a balance between the two. In our city, maximum cooling was achieved when ~60% of land was left natural and distributed in 7–8 patches km −2 although this could be location dependent and further work is needed. Opportunities for urban cooling should be required in the planning process and must consider both composition and configuration at the landscape scale if cities are to build capacity for a growing population and climate change.

Ecosystem services, Land surface temperature, Land-sharing and land-sparing, Neutral landscapes, Urban heat islands
0198-9715
80-90
Osborne, Patrick E.
c4d4261d-557c-4179-a24e-cdd7a98fb2b8
Alvares-Sanches, Tatiana
Osborne, Patrick E.
c4d4261d-557c-4179-a24e-cdd7a98fb2b8
Alvares-Sanches, Tatiana

Osborne, Patrick E. and Alvares-Sanches, Tatiana (2019) Quantifying how landscape composition and configuration affect urban land surface temperatures using machine learning and neutral landscapes. Computers, Environment and Urban Systems, 76, 80-90. (doi:10.1016/j.compenvurbsys.2019.04.003).

Record type: Article

Abstract

The urban heat island effect is an important 21st century issue because it intersects with the complex challenges of urban population growth, global climate change, public health and increasing energy demand for cooling. While the effects of urban landscape composition on land surface temperature (LST) are well-studied, less attention has been paid to the spatial arrangement of land cover types especially in smaller, often more diverse cities. Landscape configuration is important because it offers the potential to provide refuge from excessive heat for both people and buildings. We present a novel approach to quantifying how both composition and configuration affect LST derived from Landsat imagery in Southampton, UK. First, we trained a machine-learning (generalized boosted regression) model to predict LST from landscape covariates that included the characteristics of the immediate pixel and its surroundings. The model achieved a correlation between predicted and measured LST of 0.956 on independent test data (n = 102,935) and included predictors for both the immediate and adjacent land use. In contrast to other studies, we found adjacency effects to be stronger than immediate effects at 30 m resolution. Next, we used a landscape generation tool (Landscape Generator) to alter landscape configuration by varying natural and built patch sizes and arrangements while holding composition constant. The generated neutral landscapes were then fed into the machine learning model to predict patterns of LST. When we manipulated landscape configuration, the average city temperature remained the same but the local minima varied by 0.9 °C and the maxima by 4.2 °C. The effects on LST and heat island metrics correlated with landscape fragmentation indices. Moreover, the surface temperature of buildings could be reduced by up to 2.1 °C through landscape manipulation. We found that the optimum mix of land use types is neither at the land-sharing nor land-sparing extremes, but a balance between the two. In our city, maximum cooling was achieved when ~60% of land was left natural and distributed in 7–8 patches km −2 although this could be location dependent and further work is needed. Opportunities for urban cooling should be required in the planning process and must consider both composition and configuration at the landscape scale if cities are to build capacity for a growing population and climate change.

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More information

Accepted/In Press date: 12 April 2019
e-pub ahead of print date: 17 April 2019
Published date: 1 July 2019
Keywords: Ecosystem services, Land surface temperature, Land-sharing and land-sparing, Neutral landscapes, Urban heat islands

Identifiers

Local EPrints ID: 431202
URI: http://eprints.soton.ac.uk/id/eprint/431202
ISSN: 0198-9715
PURE UUID: f032c05e-5b95-4846-a6b5-49231b854d9c
ORCID for Patrick E. Osborne: ORCID iD orcid.org/0000-0001-8919-5710

Catalogue record

Date deposited: 24 May 2019 16:30
Last modified: 07 Oct 2020 04:08

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