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Characterizing bi-temporal patterns of land surface temperature using landscape metrics based on sub-pixel classifications from Landsat TM/ETM+

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journal contribution
posted on 2015-10-13, 09:51 authored by Y. Zhang, Heiko Balzter, C. Zou, H. Xu, F. Tang
Landscape patterns in a region have different sizes, shapes and spatial arrangements, which contribute to the spatial heterogeneity of the landscape and are linked to the distinct behavior of thermal environments. There is a lack of research generating landscape metrics from discretized percent impervious surface area data (ISA), which can be used as an indicator of urban spatial structure and level of development, and quantitatively characterizing the spatial patterns of landscapes and land surface temperatures (LST). In this study, linear spectral mixture analysis (LSMA) is used to derive sub-pixel ISA. Continuous fractional cover thresholds are used to discretize percent ISA into different categories related to urban land cover patterns. Landscape metrics are calculated based on different ISA categories and used to quantify urban landscape patterns and LST configurations. The characteristics of LST and percent ISA are quantified by landscape metrics such as indices of patch density, aggregation, connectedness, shape and shape complexity. The urban thermal intensity is also analyzed based on percent ISA. The results indicate that landscape metrics are sensitive to the variation of pixel values of fractional ISA, and the integration of LST, LSMA. Landscape metrics provide a quantitative method for describing the spatial distribution and seasonal variation in urban thermal patterns in response to associated urban land cover patterns.

History

Citation

International Journal of Applied Earth Observation and Geoinformation, 2015, 42, pp. 87-96 (10)

Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Geography/GIS and Remote Sensing

Version

  • AM (Accepted Manuscript)

Published in

International Journal of Applied Earth Observation and Geoinformation

issn

0303-2434

Acceptance date

2015-06-05

Copyright date

2015

Available date

2017-07-08

Publisher version

http://www.sciencedirect.com/science/article/pii/S0303243415001361

Notes

The file associated with this record is under a 24-month embargo from publication in accordance with the publisher's self-archiving policy, available at http://www.elsevier.com/about/company-information/policies/sharing. The full text may be available in the publisher links provided above.

Language

en

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