Evaluating contributions of urbanization and world local weather change to city land floor temperature change: a case research in Lagos, Nigeria

Intruduction to strategies

The principle goal of this research is to develop a normal technique to estimate the contributions of localized urbanization and world local weather change on ULSTs and take Lagos as a case research to use this technique for estimations. It’s well-known that world warming and urbanization are the 2 key contributors to the upper LST in cities38. As well as, the interannual local weather fluctuation additionally performs a job within the annual LSTs. As a way to notice the goals of this research, we now have to separate the contributions of those three elements to the annual LSTs in a metropolis.

It may be moderately assumed that the annual (imply, min., or most) LSTs will fluctuate round a relentless worth attributable to interannual local weather variations if there is no such thing as a world warming or urbanization. Additionally it is assumed that each world warming and urbanization contribute linearly to the LST enhance in city areas over time. Due to this fact, a linear regression of the annual LST time sequence will take away the contribution of interannual local weather variations to the annual LST, and the linear perform obtained from regression represents the long-term systematic development of the LST for a metropolis space, which represents the mixed contributions of each the city growth and native influence of world warming. Though the development of temperature enhance attributable to world warming varies globally, the tendencies for the 2 close by areas inside the identical local weather regime must be comparable if not the identical. That’s the reason we choose a close-by space, which has been neither subjected to urbanization nor skilled a big land use/land cowl change, because the reference web site for quantifying the contribution of world warming. The time sequence of annual LSTs and the linear regression are additionally calculated for the reference web site. The linear perform obtained from the regression on the reference web site represents the development of LST change attributable to world warming. The development from the reference web site can be utilized to approximate the development of LST change attributable to world local weather change in city areas. Due to this fact, it may be assumed that the distinction within the linear features between the city research space and the agricultural reference web site could be attributed to the contribution of urbanization to ULST change.

Thus, the contributions of each localized urbanization and world warming could be quantified by the next steps: (1) calculating annual time-series LSTs for each the city research space and the reference web site; (2) utilizing linear regressions to take away the interannual variation from the time sequence of each the research space and the reference web site. The linear features ensuing from linear regression characterize the long-time tendencies of LSTs within the research space and the reference web site, respectively; (3) For the city research space, calculating the systematic development of LST contributed by the mixture of city growth and native influence of world warming as proven in Eq. (1); and (4) deriving LST enhance contributed by urbanization by way of eradicating the contribution of world warming from the systematic development of LST within the reference web site as proven in Eq. (2).

Based mostly on the dialogue above, ((Delta {T}_{U})) is the mixture of the UHI impact brought on by urbanization and the native influence of world warming. Thus,

$$Delta {T}_{U}=Delta {T}_{UHI}+{Delta T}_{G}$$


the place (Delta {T}_{U}) denotes the general systematic ULST change from the preliminary 12 months to the ultimate 12 months, (Delta {T}_{UHI}) means the LST change as a result of urbanization impact, and ({Delta T}_{G}) is the general LST change attributable to world warming. The values of (Delta {T}_{UHI}) thus could possibly be calculated by

$$Delta {T}_{UHI}={Delta T}_{U}-{Delta T}_{G}$$


Due to this fact, to derive (Delta {T}_{UHI}), we now have to acquire ({Delta T}_{G}) first by discovering a area that’s not solely shut sufficient to town in order that the native influence of world warming is comparable by being inside the identical or comparable climatic zone, but in addition far sufficient from town in order that its LST will not be impacted by the urbanization within the metropolis. This area known as the reference web site, which is normally chosen from the close by rural areas. Additional, this web site and its quick neighbor shouldn’t have any important land use and land cowl change (LULCC) over an extended time period. As such, the systematic LST change on this web site is almost definitely to be linked to exterior atmospheric forcing, i.e., world local weather change. The annual LST time sequence for the reference area then is calculated from the MODIS LST merchandise and the linear regression on the time sequence is carried out to take away the interannual variability. Then, ({Delta T}_{G}) could be obtained by

$${Delta T}_{G}={T}_{Rf}-{T}_{Ri}$$


the place ({T}_{Rf}) and ({T}_{Ri}) are the LSTs of the reference area on the last time and the preliminary time of the time sequence, respectively, obtained by way of the linear regression equation.

Determine 6 gives the graphic description that explains the tactic on this research. Within the determine, the curves characterize the time sequence of annual particular LSTs for the city research space (crimson) and the agricultural reference space (inexperienced). The straight dot strains are the minimal imply sq. error (MMSE) linear becoming obtained by way of linear regression because the tendencies of the LST time sequence for the city (crimson) and rural areas (inexperienced). ({Delta T}_{U}) (in darkish) is the systematic LST change in the course of the research interval for the city space, which is the distinction between the fitted worth of ULST on the last time of the time sequence and that worth on the preliminary time of the time sequence. ({Delta T}_{G})(in purple) is the contribution of world local weather change to ({Delta T}_{U}). ({Delta T}_{G}) is calculated by the temperature distinction between the fitted worth of LST on the last time and that worth on the preliminary time of the time sequence for the agricultural space. (Delta {T}_{UHI})(in crimson), the contribution of urbanization to long-term ULST change (boxtimes), is calculated by the distinction between ({Delta T}_{U}) and ({Delta T}_{G}).

Determine 6
figure 6

A mixed diagrams to depict the strategies on this research.

LST knowledge acquistition

One efficient technique to measure LST is thermal distant sensing. On this research, we use two every day MODIS Land Floor Temperature & Emissivity merchandise, MOD11A1 (model 6) and MYD11A1 (model 6), from the Nationwide Aeronautics and Area Administration (NASA). The MOD11A1 every day LST product, obtainable from February 24, 2000, is derived from the MODIS sensor onboard the Terra satellite tv for pc. And the MYD11A1 every day LST product, obtainable from July 04, 2002, is derived from the MODIS sensor onboard the Aqua satellite tv for pc. Thereby, the MODIS LSTs are compiled from January 01, 2003 to December 30, 2021. Each Terra and Aqua MODIS merchandise present every day world protection (http://modis.gsfc.nasa.gov). They’ve completely different native overpass instances, i.e., Terra descending round 10:30 and ascending round 22:30 at native time, Aqua descending round 01:30 and ascending round 13:30 at native time, which permit the 2 MODIS sensors to look at the Earth floor 4 instances per day at 01:30, 10:30, 13:30, and 22:30 native time. Clouds and different atmospheric disturbances typically obscure components of and even all the statement scene, which is a big impediment to repeatedly monitor or predict LST adjustments, particularly in tropical areas23. The Terra and Aqua MODIS LST merchandise are solely captured beneath cloud-free circumstances32. Due to this fact, knowledge availability of LSTs might affect the accuracy of the collected annual LST estimations23,39.

Google Earth Engine (GEE) platform is used to entry every day LST time sequence, convert LSTs models, and calculate the regional common LSTs for the city research space and rural reference web site. Particularly, the every day daytime and nighttime LSTs (LST_Day_1km and LST_Night_1km) are chosen firstly from MOD11A1 and MYD11A1, respectively. Subsequent, the LST values are transformed from Kelvin to Celsius models utilizing the next system:



the place ({T}_{c}) is the temperature in Celsius (°C), ({T}_{ok}) is the scaled absolute temperature in Kelvins (Ok) saved within the MODIS LST merchandise, and 0.02 is a scale issue. After which an current GEE perform is utilized to calculate a single cumulative worth of the imply/max./min. LSTs in the course of the research time interval. Lastly, the boundary polygons that decided the city research space and rural reference area are uploaded to clip the corresponding LST values after which export these LSTs to Google Drive.

LST knowledge availability

When deriving meteorological parameters from remote-sensing time sequence, these satellite tv for pc observations are anticipated to be offered in good high quality40. As a result of cloud cowl and climate circumstances, the remotely sensed LST time-series merchandise, particularly within the tropical areas, include each spatial and temporal gaps and lacking values, which may trigger undesirable uncertainties within the evaluation. To extend the spatial/temporal protection of LST within the Lagos space, mixed every day diurnal MODIS LSTs from each Terra and Aqua satellites are derived on this research to generate the daytime and nighttime LST time sequence. As a result of the MYD11A1 product is out there from July 2002, which is later than the obtainable date of MOD11A1, the time interval of information assortment for this research is thus set to the time interval from the primary day of 2003 to the final day of 2021. Because the total temporal interval covers a complete of 6940 days (19 years) in addition to 4 statement instances every day, the quantity of remotely sensed LST photographs is large to be processed and calculated. Due to this fact, the Google Earth Engine (GEE) (https://earthengine.google.com/platform/), which is a cloud-based platform for a wide range of geospatial analyses, is carried out to gather and course of time-series every day imply, most and minimal ULSTs and RLSTs from daytime and nighttime MOD11A1 and MYD11A1 merchandise.

As launched above, Lagos is situated inside a tropical local weather zone characterised by excessive year-round temperatures and ample seasonal precipitation. As a result of frequent cloud cowl on this area, satellite-observed LST knowledge aren’t obtainable for every day. Significantly in the course of the moist seasons, LST photographs are solely obtainable for a number of days every month. Statistics from LSTs of these few days are hardly consultant of the LSTs of the month, which may lead to inaccurate LSTs in month or 12 months and additional estimations41. To confirm and validate this subject, we additional depend the specipic numbers of cloud-free LST photographs from MODIS at 4 statement instances for every day within the years 2003–2021 for each research websites. Determine 7 depicts the overall numbers of LSTs collected at 4 statement instances for every month of the 12 months all through the research interval throughout the city space (left plots) and the agricultural reference web site (proper plots), respectively. The upper collections could be present in Jannuary, December, and November. Though satellite tv for pc distant sensing is a wonderful knowledge supply for monitoring the Earth floor traits on a big scale42. It’s nonetheless difficult to retrieve the satellite-observed land floor traits within the tropics. Due to this fact, for this research, we solely compile every day time-series LSTs in January, November, and December for the city and reference websites to calculate the annual dry-season LSTs. For simplicity, we use the annual LSTs to characterize the annual dry-season LSTs on this research. The information processing and outcomes acquisition are in accordance with the strategies mentioned above.

Determine 7
figure 7

The numbers of information assortment by month throughout the city space (left plots) and rural reference space (proper plots) for the years of 2003 to 2021 at 4 statement instances.

All uncooked knowledge used on this research, together with MOD11A1 (v6), MYD11A1 (v6), and MCD12Q1 (v6), are freely obtainable and on-line accessible from the next hyperlinks:

Supply hyperlink