The value of vegetation cover for ecosystem services in the suburban context

Latin-American cities can be characterized by dynamic processes of urbanization that encroach upon the natural and semi-natural surrounding landscapes. Our study presents the effects of landscape development, transformed from semi-natural conditions into a mostly disperse suburban settlement. We explore the impact that this transformation has had on this context by three ecosystem services that regulate rainwater runoff, enhance microclimate conditions and help to improve air quality by monitoring vegetation cover. We have designed a spatio-temporal hierarchical analysis which employs remote sensing techniques to capture the structural changes of this landscape over long, medium and short term scales on two spatial levels. This methodological approach was tested in the Metropolitan Area of Santiago (MAS) as case study area. Despite of the increase in impervious surfaces due to urban processes, there has also been an increase in vegetation cover, which has led to an improvement in the provision of the above-mentioned ecosystem services. Hence, if diverse urbanization processes continue and they are coupled with an increase in vegetation cover, the provision of ecosystem services could also expand. This phenomenon can be observed in some areas, where public and private green spaces are created and maintained. Our data analyses give evidence that certain types of suburban areas which increase the share of vegetation cover can provide daily ecological benefits for urban neighborhoods, and beyond, for adjacent areas. Moreover, suburban development can successfully provide ecological benefits to citizens. Such processes can only be ecologically sustainable if the composition of vegetation is well-adapted to the regional climatic conditions.     

Urban forest structure and land cover composition effects on land surface temperature in a semi-arid suburban area

Using multispectral imagery and LiDAR data, we developed a high-resolution land cover dataset for a semi-arid, Colorado (USA) suburb. These data were used to evaluate patterns of land cover composition and vertical structure in relation to land use and age of development. Landsat 5 TM thermal band data for six separate dates were used to compare land surface temperature (LST) in urbanized and remnant shortgrass steppe reference areas. We used 2010 census blocks to extract LST and various explanatory variables for use in Random Forest models evaluating the relative importance of land cover composition, LiDAR-derived vertical structure variables, and the Normalized Difference Vegetation Index (NDVI) on LST patterns.We found that land cover, vertical structure, and LST varied between areas with different land use and neighborhood age. Older neighborhoods supported significantly higher tree cover and mean tree height, but differences in LST were inconsistent between Landsat image dates. NDVI had the highest variable importance in Random Forests models, followed by tree height and the mean height difference between trees and buildings. Models incorporating NDVI, vertical structure, and land cover had the highest predictive accuracy but did not perform significantly better than models using just vertical structure and NDVI. Developed areas were cooler on average than shortgrass steppe reference areas, likely due to the influence of supplemental irrigation in urbanized areas. Patterns of LST were spatially variable, highlighting the complex ways land cover composition and vertical structure can affect urban temperature.