A comparative analysis of high spatial resolution IKONOS and WorldView-2 imagery for mapping urban tree species

Urban forest tree species mapping has benefitted from advances in remote sensing techniques. In this study, we explored the potential of the newly developed high resolution satellite sensor, WorldView-2 (WV2) imagery for identifying and mapping urban tree species/groups in the City of Tampa, FL, USA by comparing capabilities between high resolution IKONOS (IKO, acquired on April 6, 2006) and WV2 (acquired on May 1, 2011) imagery for identifying the urban tree species. Seven urban tree species/groups were mapped, including: sand live oak (Quercus geminata), laurel oak (Q. laurifolia), live oak (Q. virginiana), pine (species group), palm (species group), camphor (Cinnamomum camphora), and magnolia (Magnolia grandiflora). Image-objects (IOs) were used as the tree species mapping unit. A stepwise masking protocol was developed to separate sunlit and shadow/shaded tree canopy IOs from the study area prior to tree species mapping. Comparative analyses examined average accuracies of tree species mapping results between four-band IKO imagery and three different band combinations of WV2 imagery: four “traditional” bands, four additional bands, and all eight bands. Linear Discriminant Analysis and Classification and Regression Trees were used to classify IOs using selected IO features derived from IKO and band combinations of WV2 imagery. With the exception of sand live oak mapping result (due to phenological difference due to image collection dates), validation results indicate significantly improved mapping accuracies using all combinations of WV2 imagery (p < 0.01). Results using independent validation samples demonstrated that average accuracy was increased by 16–18% using WV2 imagery compared to that using IKO imagery when considering mapping 6-species/group. Improved results with the WV2 sensor could be attributed to improved spatial resolution (4 m to 2 m) and additional bands (coastal, yellow, red-edge and NIR2).

► High spatial/spectral resolution data are vital to mapping tree species.
► WorldView-2 has a great potential to improve the vegetation classification.
► WorldView-2 outperforms IKONOS for identifying tree species/groups.
► A stepwise masking system is effective for mapping tree species.