Long-Term Spatial–Temporal Analysis of Land Cover and Land Surface Temperature Changes in Chatham County, Georgia

As populations grow, cities are challenged to make decisions on how to accommodate growth while guaranteeing the health and well-being of its inhabitants in the face of a changing climate. Studies in urban climatology strive to further understand the interactions that occur in cities to better transfer knowledge to fields such as urban planning, and support climate-oriented decision-making. This study presents the analysis of the impacts of land use/cover on land surface temperatures in Chatham County, in the coast of Georgia, in the United States. It seeks to aid in the interpretation of how cities impact climate by analyzing the occurrence of surface urban heat island. Furthermore, it attempts to overcome struggles of understanding the relationships between land use/ land cover and land surface temperature, as described in previous studies in the fields of remote sensing and urban climatology.

The methodology applied to understand the spatial and temporal changes that have occurred in Chatham County, Georgia, focuses on an analysis of the National Land Cover Database (NLCD) and Landsat 5 Surface Reflectance (Tier 1) images, performed using the Google Earth Engine platform. The use of this tool enabled the analysis of approximately 299 images for the period between 1992 to 2011. The choice of analysis period was determined by the NLCD database, with available land cover datasets for 1992, 2001 and 2011. Therefore, Google Earth Engine is applied as a tool to retrieve, process and synthesize satellite imagery to understand the long-term spatial-temporal relationships of land use/cover and land surface temperature variations.

This methodology presents a path to the analysis of changes in land surface temperature and land use and land cover data over a 20-year period. This approach is novel and enables a robust spatial-temporal analysis that moves beyond the assessment of land surface temperature, and further, explores their relationships to land use and land cover. This study seeks to indicate that a combined spatial-temporal analysis of land cover and land surface temperature changes can illustrate that certain types of land use and land cover changes are linked to higher thermal impacts. Furthermore, these findings could also point to adaptation measures by signaling the land cover types that are responsible for lower surface temperatures. Mainly by comparing changes in land cover to changes in land surface temperature, the study further links urbanization to surface heating, depicting the occurrence of surface urban heat island and its potential intensification over time.

The analysis of land cover indicates an increase in urbanization and impervious cover over the 20-year study period. While the analysis of land surface temperature depicts an increase in temperatures across all surfaces during the studied period, with higher temperatures found on developed land cover types. Furthermore, statistical analysis indicated that surface temperature changes were significantly higher in areas where forest cover was loss due to urbanization. The results from this study align with previous studies that have attempted to develop a long-term spatial-temporal analysis of land surface temperature and land cover changes. It shows that the highest land surface temperature changes occur in land use/cover changes from forested land to urban-medium intensity land cover types. Additionally, the study corroborates previous conclusions that indicate that vegetation abundance and impervious cover are important determinants to the intensification of surface urban heat island and land surface temperature increases over time.

The results also point to linkages with decision-making and identify areas where loss of tree canopy cover and wooded areas have produced high temperature shifts when developed on. It points to the importance of tree canopy cover, and further aligns with studies that suggest the need for tree cover contiguity within the urban network in order to reduce the occurrence of surface urban heat island. The data and knowledge produced in this study could serve as insight for the creation of heat-oriented plans that seek adaptation measures that promote intra-urban greening. Moreover, this research points to the need for further investigation on the usability of the data created and its potential applications not only as a decision-making tool, for fields such as land-use planning and urban design, but also in the development of a heat vulnerability index that addresses the temporal aspects of urban heating.