“Global warming” and “climate change” are no longer novel concepts. To meet these challenges, our ability to adapt quickly will be crucial.
Why do we need to focus on urban areas?
Around the world, efforts are on to find ways of adapting to climate change. Out of all the adaptation efforts, those intended for cities need to speed up really fast. It is because we know that a “heat island” is developed due to the cities, as they are warmer than the areas around them. That is why urban and peri-urban areas must be the specific focus of interventions.
What questions do urban planners have?
It is a well-known fact that trees are one of the best interventions that can aid in adapting to climate change. Thus, it is required that urban afforestation efforts be strategized efficiently.
However, the question of whether urban afforestation truly has an impact may arise for urban planners. By investing in urban afforestation, how much is it helping their city? Is there a way for them to maximize the cooling effects they achieve?
Do we have any answers?
Fortunately, we have technologies for answering such questions posed by urban planners. Let us explore how remote sensing-based technology can help us study the effect of urban afforestation efforts.
Determining the effectiveness of trees in cooling urban environment
There is a phrase known as tree cooling efficiency (TCE). It is defined as, in any given region, how much of a drop in temperature is achieved through a one percent increase in tree cover. TCE can be calculated for any particular region if we can assess the change in tree-covered areas and the corresponding change in temperature over the years.
From the remote sensing point of view, in essence, two things are done using datasets from multiple dates: one is to get the coverage of urban trees, and the other is to obtain the remotely sensed land surface temperature.
Scholars have employed datasets, including those obtained from MODIS and Landsat. The worldwide continuous tree cover data from MODIS has a 250 m spatial resolution, while the vegetation continuous fields (VCF) data product from Landsat has a 30 m resolution. For LST, MODIS-based daily LST data at 1km resolution is often used.
After obtaining information on the tree cover change and LST, a relationship between the two is discovered using the linear regression method. Machine learning algorithms like boosted regression trees (BRT) have shown their utilization in such analyses.
To establish a more thorough relationship with LSTs, several researchers have additionally looked into the dynamics of land use and land cover. Different types of land cover make different contributions. This information is also derived from a time series of remote sensing satellite images.
Which factors affect the cooling the trees provide?
- Leaf area index (LAI): It is the quantification of the amount of leaf material in a plant canopy. The cooling effect of trees varies with different LAIs. An increase in LAI improves the TCE. In this way, the effectiveness of a green space with or without trees surely varies.
- Climate-related factors also affect the cooling efficiency of the trees.
- Anthropogenic elements, like the albedo of a city, which is its ability to reflect sunlight, also affects the cooling effect. If the city albedo drops, the tree cooling efficiency improves.
- Spatial heterogeneity needs to be considered, both three-dimensional and two-dimensional.
- Cities in mid-latitudes have shown a more noticeable increase in tree cooling efficiency.
Such evaluations of tree cooling effectiveness should be carried out in urban areas across the country. This would help in developing environmental standards for urban greening and afforestation initiatives.
References:
- Leng, S., Sun, R., Yan, M., & Chen, L. (2024). Prevalent underestimation of tree cooling efficiency attributed to urban intrinsic heterogeneity. Sustainable Cities and Society, 103, 105277. https://doi.org/10.1016/j.scs.2024.105277
- Schwaab, J., Meier, R., Mussetti, G., Seneviratne, S., Bürgi, C., & Davin, E. L. (2021). The role of urban trees in reducing land surface temperatures in European cities. Nature Communications, 12(1), 6763. https://doi.org/10.1038/s41467-021-26768-w
- Zhao, J., Zhao, X., Wu, D., Meili, N., & Fatichi, S. (2023). Satellite-based evidence highlights a considerable increase of urban tree cooling benefits from 2000 to 2015. Global Change Biology, 29(11), 3085–3097. https://doi.org/10.1111/gcb.16667
