Integrating urban morphology and land surface temperature characteristics for urban functional area classification

Figures & data

Figure 1. Framework of urban function classification.

Table 1. Urban morphology metrics

Level	Metric	Description
Building level	BD	Building density
	TN	Total building number
	TE	Total number of building edges
	ME	Mean number of building edges
	SDBE	Standard deviation of building edges
	MH	Mean building height
	SDBH	Standard deviation of building height
	MAXA	Max. building area
	MEANA	Mean building area
	TBA	Total building area
	SDBA	Standard deviation of the building area
Road level	TRL	Total road length
	RRB	Ratio of roads to buildings
	RRO	Ratio of roads to other features
Region level	MPFD	Mean patch fractal dimension
	LSI	Landscape shape index
	MBSI	Mean building structure index
	SDBSI	Standard deviation of the building structure index

Figure 2. Typical example of urban morphology index measurements.

Table 2. Characteristic indexes of LST

Metric	Description
MEANT	Regional average temperature
MAXT	Regional maximum temperature
RANGE	Regional temperature difference
STDT	Standard deviation of the temperature

Figure 3. Study area and experimental data.

Figure 4. LST retrieval results and functional area.

Figure 5. Box plots of urban morphology and LST metrics.

Table 3. The results of the variance analysis of urban morphological metrics and LST characteristics

	Metric	F	Sig.
Morphological metrics	BD	47.925	.000
	TN	14.190	.000
	TE	20.194	.000
	ME	9.411	.000
	SDBE	19.820	.000
	MH	80.015	.000
	SDBH	84.265	.000
	MAXA	10.370	.000
	MEANA	2.509	.000
	TBA	20.664	.000
	SDBA	2.594	.000
	TRL	34.356	.000
	RRB	2.287	.000
	RRO	22.374	.000
	MPFD	54.975	.000
	LSI	55.209	.000
	MBSI	8.215	.000
	SDBSI	8.983	.000
LST features	MEANT	152.426	.000
	MAXT	145.947	.000
	RANGE	64.415	.000
	STDT	46.665	.000

Figure 6. Hyperparameter results using a grid search method.

Figure 7. Functional area classification. (a) Actual classification. (b) Classification results based on urban morphology. (c) Classification results based on LST characteristics. (d) Classification results based on both the urban morphology and LST.

Table 4. Classification accuracy of urban functional areas based on morphological, LST, and integrated models

Classification model	Morphological model	LST model	Integrated model
User’s accuracy of industrial land	0.73	0.97	0.98
User’s accuracy of commercial land	0.35	0.43	0.67
User’s accuracy of residential land	0.66	0.66	0.82
User’s accuracy of service/public land	0.45	0.90	0.87
Producer’s accuracy of industrial land	0.65	0.97	0.97
Producer’s accuracy of commercial land	0.46	0.59	0.79
Producer’s accuracy of residential land	0.61	0.52	0.72
Producer’s accuracy of service/public land	0.54	0.85	0.85
Overall accuracy	0.59	0.75	0.85
Kappa	0.40	0.64	0.78

Figure 8. The relative importance of the features in the three models.

Figure 9. Comparison of the different LST times on classification accuracy.

Data availability statement

The data that support the findings of this study are available at [http://www.gscloud.cn/, https://www.openstreetmap.org/], These data were derived from the following resources available in the public domain: [http://www.gscloud.cn/, https://www.openstreetmap.org/]

The building and real functional area data that support the findings of this study are available from the corresponding author, [Hanfa Xing], upon reasonable request.