Spatial estimation of urban forest structures with Landsat TM data and field measurements

Urban forest structure is an important variable that influences urban forest ecosystem function across the landscape. However, it is generally labor-intensive and time-consuming to obtain urban forest structural attributes with the traditional field sampling methods. This study explores the potential of using Landsat-5 Thematic Mapper (TM) imagery in estimating urban forest structural attributes including stem density, diameter at breast height, tree height, leaf area index, canopy density and basal area. In our study, three vegetation indices including normalized difference vegetation index (NDVI), simple ratio (SR), and green normalized difference vegetation (GNDVI), obtained from Landsat-5 Thematic Mapper (TM) data and urban forest structure data derived from the field-based survey were used to develop a regression model to predict the selected urban forest structures in Changchun, China. Finally, an urban forest structure map was produced from the vegetation indices map by using the regression models based on measured urban forest structural attributes and vegetation indices. The results show that NDVI is better than SR and GNDVI for predicting the selected urban forest structural attributes. But some forest structure metrics that can be predicted well in natural forests by NDVI cannot be predicted for urban forests. Canopy density, basal area and leaf area index were strongly related to NDVI. Stem density, diameter at breast height and tree height were not related to NDVI. In Changchun city, leaf area index, canopy density and basal area of the urban forest all show a definite gradient decreasing from suburban areas to urban center areas. The canopy density, basal area and leaf area index class distribution were all skewed toward low values. The results demonstrate Landsat TM has a relatively rapid and efficient capability for quantitative estimation of some urban forest structural attributes including leaf area index, canopy density and basal area over urban areas.