The purpose of this lesson is to help prepare you for your final project, moving from extracting information from remotely sensed data to GIS-based analysis using remotely sensed thermal imagery and information obtained through automated feature extraction. The issue you will examine is the urban heat island. Although climate change often receives the most attention with respect to the warming of the Earth, land use conversion, in which vegetation is replaced by impervious surfaces, can have a much greater effect at the local level. Paved surfaces absorb heat, causing urbanized areas to be significantly warmer than the surrounding countryside. While any type of vegetation can reduce the urban heat island, trees are particularly valuable due to the amount of heat they remove through transpiration.
Zonal analysis is a GIS operation often applied to aggregate remotely sensed data, or data derivatives, using preexisting vector polygons (zones). An example would be summarizing NDVI for wildlife management units or impervious surfaces by property parcels. The zonal operation allows the relationship to be examined between the remotely sensed information and the GIS data. In the case of summarizing NDVI by wildlife management unit, the analysis might focus on the correlation between vegetation biomass and the presence of grazing animals. Summarizing impervious surfaces at the parcel level could be used by a municipality to see if zoning regulations influence the number of impervious surfaces.
At the end of this lesson you will be able to:
- integrate remotely sensed and GIS data;
- apply zonal functions to summarize raster information by vector polygons;
- perform a qualitative and qualitative analysis of spatial data.
If you have any questions now or at any point during this week, please feel free to post them to the Lesson 7 Questions and Comments Discussion Forum in Canvas.