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

Abstract

16Landscape patterns in a region have different sizes, shapes and spatial arrangements, 17 which contribute to the spatial heterogeneity of the landscape and are linked to the distinct 18 behavior of thermal environments. There is a lack of research generating landscape metrics 19 from discretized percent impervious surface area data (ISA), which can be used as an 20 indicator of urban spatial structure and level of development, and quantitatively characterizing 21 the spatial patterns of landscapes and land surface temperatures (LST). In this study, linear 22 spectral mixture analysis (LSMA) is used to derive sub-pixel ISA. Continuous fractional 23 cover thresholds are used to discretize percent ISA into different categories related to urban 24 land cover patterns. Landscape metrics are calculated based on different ISA categories and 25 used to quantify urban landscape patterns and LST configurations. The characteristics of LST 26 and percent ISA are quantified by landscape metrics such as indices of patch density, 27 aggregation, connectedness, shape and shape complexity. The urban thermal intensity is also 28 analyzed based on percent ISA. The results indicate that landscape metrics are sensitive to the 29 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 2 variation of pixel values of fractional ISA, and the integration of LST, LSMA. Landscape 30 metrics provide a quantitative method for describing the spatial distribution and seasonal 31 variation in urban thermal patterns in response to associated urban land cover patterns. 32 *Manuscript Click here to view linked References

Measuring the Urban Land Surface Temperature Variations Under Zhengzhou City Expansion Using Landsat-Like Data

“…Different LULC types had different specific heat capacities, and vegetation was transferred to built-up land, which increased LST [69]. This is consistent with the results of previous studies [70], indicating that ISA changes have a large impact on the spatial distribution of LST.…”

Measuring the Urban Land Surface Temperature Variations in Zhengzhou City Using the Landsat-Like Data

“…From this data, it can be concluded that the LST significantly rises when other types of LULC are converted to ISA, and that it is relatively stable in areas where the ISA shows No Change, but in regions where the ISA decreased, the LST showed a significant negative trend. This is consistent with the results of previous studies [40,41], further indicating that ISA regional changes have a large impact on the spatial distribution of LST.…”

Characterizing Urban Fabric Properties and Their Thermal Effect Using QuickBird Image and Landsat 8 Thermal Infrared (TIR) Data: The Case of Downtown Shanghai, China

“…In addition to prevailing meteorological conditions across local-and meso-scales, urban fabric properties play the key role in formation of the UHI effect via complicated processes such as biophysical features of the land surfaces, building layout and wind corridor, landscape heterogeneity, energy consumption and anthropogenic heat emission [12,[39][40][41][42][43][44][45]. In this study, we presented the differential thermal effect of urban fabric properties, i.e., LST, UHI intensity and BBFD associated with varying land surfaces at UFZ level.…”