
==== Front
Data Brief
Data Brief
Data in Brief
2352-3409
Elsevier

S2352-3409(24)00812-6
10.1016/j.dib.2024.110848
110848
Data Article
Gridded dataset on land surface temperature and selected environmental and socioeconomic features in Southeast Asian metropolises
Pagkalinawan Homer a
Delina Laurence L lld@ust.hk
@laurencedelina
b⁎
Macagba Sharon Feliza Ann bc
a Macroeconomics Research Division, Asian Development Bank, Mandaluyong City, Philippines
b Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
c Department of Community and Environmental Resource Planning, College of Human Ecology, University of the Philippines Los Banos, Laguna, Philippines
⁎ Corresponding author. lld@ust.hk@laurencedelina
29 8 2024
12 2024
29 8 2024
57 11084814 5 2024
4 8 2024
12 8 2024
© 2024 The Author(s)
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
As Southeast Asia grapples with extreme heat occurrences in recent years, mapping which areas are clustered with elevated temperatures is crucial for monitoring the at-risk population. Identifying the contributing factors to the warming trends in these areas is also vital in formulating adaptation and mitigation strategies. This dataset comprises land surface temperature (LST) in three metropolises in the region – Metropolitan Manila, Bangkok Metropolitan Area, and Greater Jakarta – downloaded and processed from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. We used MODIS’ inherent grid system to map LST values at the satellite image's most granular level. We combined them with selected environmental and socioeconomic variables, including building and built-up areas, areas of greeneries, industrial zones, and water bodies, nighttime light (to approximate areas of economic activities), gridded population, distance from water bodies, and indicators on which urban infrastructures, i.e. roads and airports, are present in each grid. Available in shapefile and comma-separate variable file format, this dataset is useful for urban studies in these three cities. The dataset can be easily updated as additional data on LST and other variables becomes available.

Keywords

Land surface temperature (LST)
Extreme heat
MODIS
Southeast Asia
==== Body
pmcSpecifications TableSubject	Environmental Sciences.	
Specific subject area	Extreme heat analysis using satellite-based land surface temperature.	
Type of data	Tables and GIS file format.	
Data collection	We downloaded and processed land surface temperature data from MODIS from February 2000 to December 2023. We complemented these observations with selected environmental and socioeconomic variables.	
Data source location	LST were downloaded using Python scripts from NASA's Level-1 and Atmospheric Archive & Distribution System Distributed Active Archive Centre. Other datasets were collected from various sources, including GIS and remote sensing data.
Secondary data sources:	
	
Data Name	Source	Link	
	
Shapefiles of greeneries, industrial areas, buildings, roads, and water bodies	OpenStreetMap/ Geofabrik	www.geofabrik.de/	
	
Nighttime light	Earth Observation Group	www.eogdata.mines.edu/products/vnl/	
	
Gridded population	World Population Research Programme	www.worldpop.org/	
	
Elevation	Shuttle Radar Topography Mission	https://dwtkns.com/srtm30m/	
	
	
Data accessibility	Repository name: Harvard Dataverse [1]
Data identification number: 10.7910/DVN/PIR5GI
Direct URL to data: https://doi.org/10.7910/DVN/PIR5GI	
Related research article	None.	

1 Value of the Data

• The dataset provides monthly LST at the most granular level of observation by MODIS’ MOD21 product for Metropolitan Manila, Bangkok Metropolitan Area, and Greater Jakarta from February 2000 to December 2023. It can be updated as additional LST observations become available. The formats provided are comma-separated values (CSV) and shapefile.

• The shapefile file format provides convenience and flexibility in performing geospatial analytics that need LST as an input. The shapefiles can be used with other geospatial data layers to process different analyses.

• We also collected select environmental and socioeconomic variables for each grid. These variables include building and built-up areas, greeneries, industrial zones, water bodies, nighttime light (to approximate areas of economic activities), gridded populations, distances from water bodies, and indicators of the presence of roads and airports.

• Using the hotspot analysis function from GEODA, an open-sourced software for geospatial analytics and spatial regression, we derived an indicator that determines clusters of neighbouring grids with high-temperature values. This is useful in identifying the extent of extreme heat in each metropolis.

• We also identified which administrative units, i.e. barangays for Metropolitan Manila, tambon for Bangkok Metropolitan Area, and kampong for Greater Jakarta, have at least 50 % of their land area within the mapped extreme heat clusters.

2 Background

2023 was identified as the hottest year on record, and 2024 is estimated to surpass it. Urban areas, where humans and built infrastructures are concentrated, are most affected by higher temperatures. In Southeast Asia, these metropolitan areas are also home to large populations, suggesting their heightened risk of extreme heat hazards. Using satellite-based land surface temperature observed from February 2000 to December 2023 by the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument [2], we collected gridded LSTs for Metropolitan Manila, Bangkok Metropolitan Area, and Greater Jakarta. We also identified extreme heat clusters in these three cities and compared environmental and socioeconomic factors to elucidate what contributes to high temperatures in these clusters.

Ahmed et al. [3] reviewed using satellite imagery to monitor trends in LSTs. 76 % of the studies included in their systematic review also used MODIS. They specified concerns about determining annual trends due to seasonal variations. The dataset included in this research consists of all available periods up to 2023. Thus, the mentioned seasonal effect can be explored when using this dataset. Ahmed et al. [3] also suggested approaches to overcoming satellite-based LST limitations. Development of data fusion techniques that include higher resolution LST data from Landsat and Advanced Very High-Resolution Radiometer can provide the most extended LST observations with higher spatial resolution.

3 Data Description

The dataset comprises the following files for each metropolis: (1) a CSV file of grid-based LST, (2) a shapefile format of grid-based LST and selected environmental and socioeconomic variables, and (3) a shapefile of administrative units for each metropolis with additional indicator on extreme heat clusters. A geographic information system (GIS) software or similar applications capable of reading shapefile format, e.g. Python or R, is needed to open these files. The columns for each file are described in Table 1, Table 2, Table 3 and 3.Table 1 Overview of the gridded LST CSV file.

Table 1	Column Header	Description	
1	Id	Identifier for each grid observation of LST.	
2	Date with format of YYYY-MM	Average monthly LST in each grid for a particular month and year. Blank values denote no LST observation is available for the month based on QC checking.	

Table 2 Overview of the gridded LST shapefile.

Table 2	Column Header in Attribute Table	Description	
1	Id	Identifier for each grid observation of LST.	
2	meanLST	Average monthly LST in each grid from February 2000 to December 2023.	
3	Green_area	Land area in hectares of greeneries in each grid.	
4	Ind_area	Land area in hectares of industrial areas in each grid.	
5	Bldg_area	Land area in hectares of building footprints in each grid.	
6	Built_area	Land area in hectares of built-up land cover in each grid.	
7	NTLsum	Sum of nighttime lights in each grid.	
8	Popsum	Sum of gridded population.	
9	Elevation	Average elevation in meters in each grid.	
10	Dist_Water	Distance from the nearest major water body in kilometres of each grid	
11	Waterbody	Dummy variable (either 0 or 1) indicating if a grid intersects an inland water body, e.g. lake or river	
12	Motorway	Dummy variable (either 0 or 1) indicating if a grid intersects a road classified as a motorway (toll road)	
13	Trunk	Dummy variable (either 0 or 1) indicating if a grid intersects a road classified as trunk	
14	Primary	Dummy variable (either 0 or 1) indicating if a grid intersects a road classified as primary	
15	Secondary	Dummy variable (either 0 or 1) indicating if a grid intersects a road classified as secondary	
16	ln_mean	Natural logarithm of value in meanLST column	
17	ln_Green	Natural logarithm of value in Green_area column	
18	ln_Ind	Natural logarithm of value in Ind_area column	
19	ln_Bldg	Natural logarithm of value in Bldg_area column	
20	ln_Built	Natural logarithm of value in Built_area column	
21	ln_NTL	Natural logarithm of value in NTLsum column	
22	ln_Pop	Natural logarithm of value in Popsum column	
23	ln_Water	Natural logarithm of value in Dist_Water column	
24	ln_Elev	Natural logarithm of value in the Elevation column	
25	EHcluster	Dummy variable (either 0 or 1) indicating if a grid is part of an extreme heat cluster	

Table 3 Overview of the administrative unit shapefile.

Table 3	Column Header in Attribute Table	Description	
1	ADM4_EN	Name of Level-4 administrative unit	
2	ADM4_PCODE	Identifier of Level-4 administrative unit	
3	ADM3_EN	Name of Level-3 administrative unit	
4	ADM3_PCODE	Identifier of Level-3 administrative unit	
5	ADM2_EN	Name of Level-2 administrative unit	
6	ADM2_PCODE	Identifier of Level-2 administrative unit	
7	ADM1_EN	Name of Level-1 administrative unit	
8	ADM1_PCODE	Identifier of Level-1 administrative unit	
9	ADM0_EN	Name of Level-0 administrative unit	
10	ADM0_PCODE	Identifier of Level-0 administrative unit	
11	AreaHa	Area in hectares	
12	Pct_cover	Percentage of area intersecting extreme heat cluster	

4 Experimental Design, Materials and Methods

Fig. 1 shows the methodology applied in processing LST from MODIS, which includes downloading, data extraction, data quality check through masking, conversion to grid value, and validation using air temperature observed by ground-based weather stations.Fig. 1 Processing of MODIS land surface temperature.

Fig 1:

We downloaded MOD21 data products from NASA's Level-1 and Atmospheric Archive & Distribution System Distributed Active Archive Centre. MOD21 contains an LST image and an accompanying quality control (QC) image. We automated the downloading and extraction process using Python scripts [4]. We also used Python to apply QC images when masking LST images. We validated the processed LST by comparing it with air surface temperature. Fig. 2 shows similar trends between LST and air temperature. Table 4 indicated a moderate to strong correlation between LST and air temperature among the three metropolitan areas.Fig. 2 Validation using Air Surface Temperature for (a) Metropolitan Manila, (b) Bangkok Metropolitan Area and (c) Greater Jakarta.

Fig 2:

Table 4 Correlation table between LST and air temperature.

Table 4Weather Station	Location	Correlation	
Metropolitan Manila	
Manila	14.5500, 120.9833	0.6230	
Quezon City Science Garden	14.63301, 121.0172	0.6318	
Ninoy Aquino International Airport	14.5171, 121.0007	0.6269	
Average		0.6387	
Bangkok Metropolitan Area	
Bangkok Metropolis	3.7330, 100.5670	0.6921	
Don Muang Airport	13.7330, 100.5670	0.6538	
Bang Na Agrometeorological	13.6667, 100.6170	0.7568	
Average		0.6878	
Greater Jakarta	
Jakarta Observatory	−6.1830, 106.8330	0.0694	
Tanjung Priok	−6.1000, 106.8670	0.2985	
Soekarno Hatta International Airport	−6.1260, 106.6560	0.4291	
Budiarto	−6.2930, 106.5700	0.1892	
Average		0.3346	

We converted an LST image for each metropolis to a 1 km ×1 km grid shapefile. We used this grid shapefile to extract historical values of LST and converted them to a CSV file. Similarly, we extracted environmental and socioeconomic variables for each grid. Using shapefile from OpenStreetMap [5], we determined the size of features identified as greeneries and industrial zones per grid. We also used road shapefile from OpenStreetMap [5] to create dummy variables, with values at either 0 or 1, to indicate intersect roads identified as motorway, trunk, primary, and secondary, airports, and inland water bodies, e.g., rivers and lakes. We used the world footprint settlement data from Marconcini et al. [6] and the built-up land cover from the Environmental Systems Research Institute [7] to estimate building cover and built area per grid. We also extracted data on the sum of nighttime lights from Elvidge et al. [8], the sum of the gridded population from the World Population Research Programme [9], and the average elevation from the United States Geological Survey's Shuttle Radar Topography Mission [[10], [11]]. Lastly, we computed the distance of each grid from the nearest primary water body.

Except for dummy variables, we created columns containing the natural logarithm of average LST per grid and environmental and socioeconomic variables to conform to the standard distribution requirement needed for regression analysis. We also added a column that indicates whether a grid is included in an extreme heat cluster or a group of neighboring grids with similarly high values.

We identified intersecting administrative units [[12], [13], [14]] to provide locational context for these extreme heat clusters. An administrative unit is included if at least 50 % of its land area intersects with the extreme heat clusters.

Other data providers and processors, e.g., Google Earth Engine, use the same MODIS source. However, our dataset includes collected ancillary data listed in Table 2.

Limitations

The dataset is limited to only the LST observations from MODIS from February 2000 to December 2023, and at the grid level, months without available observations passed quality control checks. The 1 km × 1 km projected MODIS resolution is also lower than other LST sources, such as the Landsat Data Continuity Mission and Sentinel. The selected environmental and socioeconomic variables were limited to the dataset available during the data generation and can change as newer data becomes available from the abovementioned sources. Boundaries of administrative units are not authoritative.

Ethics Statement

The current work does not involve human subjects, animal experiments, or any data collected from social media platforms.

CRediT authorship contribution statement

Homer Pagkalinawan: Conceptualization, Methodology, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization. Laurence L Delina: Conceptualization, Supervision, Writing – review & editing. Sharon Feliza Ann Macagba: Validation, Writing – review & editing.

Data Availability

Gridded Land Surface dataset (Original data) (Dataverse).

Acknowledgments

This research received no specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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