Spatiotemporal Analyses of Urban Vegetation Structural Attributes using Multitemporal Landsat TM Data and Field Measurements

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Spatiotemporal analysis, Urban vegetation structure, Landsat TM imagery, NDVI

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Key message: We conducted spatiotemporal analyses of urban vegetation structural attributes using multitemporal Landsat TM data and field measurements. We showed that multitemporal TM data has the potential of rapidly estimating urban vegetation structural attributes including LAI, CC , and BA at an urban landscape level.

Context: Urban vegetation structural properties/attributes are closely linked to their ecological functions and thus directly affect urban ecosystem process such as energy, water, and gas exchange. Understanding spatiotemporal dynamics of urban vegetation structures is important for sustaining urban ecosystem service and improving the urban environment.

Aims: The purposes of this study were to evaluate the potential of estimating urban vegetation structural attributes from multitemporal Landsat TM imagery and to analyze spatiotemporal changes of the urban structural attributes.

Methods: We first collected three scenes of TM images acquired in 1997, 2004, and 2010 and conducted a field survey to collect urban vegetation structural data (including crown closure (CC), tree height (H), leaf area index (LAI), basal area (BA), stem density (SD), diameter at breast height (DBH), etc.). We then calculated and normalized NDVI maps from the multitemporal TM images. Finally, spatiotemporal urban vegetation structural maps were created using NDVI-based urban vegetation structure predictive models.

Results: The results show that NDVI can be used as a predictor for some selected urban vegetation structural attributes (i.e., CC, LAI, and BA), but not for the other attributes (i.e., H, SD, and DBH) that are well predicted by NDVI in natural vegetation. The results also indicate that urban vegetation structural attributes (i.e., CC, LAI, and BA) in the study area decreased sharply from 1997 to 2004 but increased slightly from 2004 to 2010. The CC, LAI, and BA class distributions were all skewed toward low values in 1997 and 2004. Moreover, LAI, CC, and BA of urban vegetation all present a decreasing trend from suburban areas to urban central areas.

Conclusion: The experimental results demonstrate that Landsat TM imagery could provide a fast and cost-effective method to obtain a spatiotemporal 30-m resolution urban vegetation structural dataset (including CC, LAI, and BA).

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Citation / Publisher Attribution

Annals of Forest Science, v. 74, issue 3, art. 54