Simulating land surface temperature using biophysical variables related to building density and height in Dar Es Salaam, Tanzania

Figures & data

Figure 1. Location of the study area within a) Africa b) Tanzania, and c) Dar es Salaam Metropolitan City.

Table 1. Details of Landsat satellite imagery.

Sensor	Path	Raw	Date of Acquired
Landsat 5 (TM)	166	65	25 June 1995
Landsat 5 (TM)	166	65	01 July 2009
Landsat 8 (OLI)	166	65	25 June 2017

Table 2. Derivation of spectral index explanatory variables from Landsat data.

Spectral Index	Expression	Reference
NDBaI	(SWIR1-TIRS)/(SWIR1+TIRS)	(Zhao and Chen 2005)
SAVI	((NIR-RED)/(NIR + RED))*(1+L)	(Huete 1988)
NDBI	(SWIR1-NIR)/(SWIR1+NIR)	(Zha et al. 2003)

Figure 2. Concept of determination for built-up pixels in the urban high-density plot.

Figure 3. Spatiotemporal distribution of LST for (a) 1995, (b) 2009, and (c) 2017.

Table 3. OLS model regression results.

	1995	2009	2017
Multiple R^2	0.3593	0.352	0.4334
Adjusted R^2	0.3581	0.3508	0.4323

Table 4. Regression coefficients between LST and driving factors.

	1995			2009			2017
	NDBI	SAVI	NDBaI	NDBI	SAVI	NDBaI	NDBI	SAVI	NDBaI
Correlation	3.54***	−2.32***	0.77**	5.13***	−2.36***	1.24*	4.11***	−2.88***	−2.14***
VIF	3.32	1.76	2.95	9.80	4.38	7.28	5.18	4.83	2.91
Intercept	23.87***			23.10***			26.37***

significance level (p-value): ^*** p < 0.001; ^** p < 0.01; ^* p < 0.05

Table 5. Results of modelling biophysical variables and LST by the GWR model.

^AICC	R^2	W^ARNING	Adjusted R^2	Unbiased sigma	Sigma biased
1995	3450.55181	0.64	0.55	0.7224467	0.7083403
2009	2983.044	0.89	0.82	0.6364869	0.6096698
2017	3535.6852	0.76	0.62	0.7166683	0.6892951

Table 6. Global Moran’s I spatial autocorrelation summary for 1995, 2009, and 2017.

	1995		2009		2017
	OLS	GWR	OLS	GWR	OLS	GWR
Moran’s Index	0.296624	0.002755	0.660033	0.140143	0.212436	0.066100
Expected Index	−0.000646	−0.00065	−0.000560	0.000560	−0.000633	−0.000633
Variance	0.000185	0.000187	0.000297	0.000297	0.000176	0.000176
z-score	21.878568	0.248839	38.30695	8.166103	16.075018	5.034156
p-value	0.000000	0.803486	0.000000	0.000000	0.000000	0.000000

Figure 4. Geographical variables affecting LST in the study area: a) NDBI, b) SAVI, and c) NDBaI.

Figure 5. Distribution of residuals using GWR in the study area for a) 1995, b) 2009, and c) 2017.

Figure 6. Relationships between building density and LSTs in Dar es Salaam central business district and existing city: a) building layer, b) building density pixel classification, and c) LST. Building layer was modified from Resilience Academy - Dar es Salaam buildings (https://resilienceacademy.ac.tz).

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Zha Y, Gao J, Ni S. 2003. Use of normalised difference built-up index in automatically mapping urban areas from TM imagery. Int J Remote Sens. 24(3):583–594.

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Data availability statement

The data that support the findings of this study are available from the corresponding author, [O.S], upon reasonable request.