High spatial resolution spectral unmixing for mapping ash species across a complex urban environment

摘要

Abstract Ash (Fraxinus L.) species are currently threatened by the emerald ash borer (EAB; Agrilus planipennis Fairmaire) across a growing area in the eastern US. Accurate mapping of ash species is required to monitor the host resource, predict EAB spread and better understand the short- and long-term effects of EAB on the ash resource. Hyperspectral remote sensing technologies have been used to successfully map forest species, although most efforts are focused on healthy canopies for relatively homogeneous forested stands. This study uses imagery collected by the NASA Goddard LiDAR, Hyperspectral and Thermal (GLiHT) airborne imager to map ash species at the tree level in an EAB infested urban setting. The overall goal of the study is to understand how canopy condition impacts species mapping accuracy and identify data collection and image processing techniques to more accurately map the location of ash species in infested regions. Results indicate that while overall independent validation mapping accuracy of ash and non-ash trees was 81%, correct identification of ash canopies dropped from 62% for vigor 1 trees to 22% for vigor 2 trees. To minimize these errors, we developed a multiple endmember, spectral unmixing technique to overcome challenges presented by a spectrally complicated target in a complex urban environment. This hinges on the use of endmember spectra from trees across a range of canopy condition, including the derivation of vegetation indices to inform the spectral unmixing calibration. This approach was more accurate than calibrations performed using traditional unmixing based only on pure endmember spectra. Implications for this work suggest that urban forest managers may attain more accurate maps by conducting remote sensing data collections prior to infestation while the trees are still healthy. Where this is not possible, mapping efforts must reflect a range of canopy conditions and include vegetation indices concurrent with reflectance data. The resulting ash species maps provide urban forest managers spatially explicit products to help estimate the extent of possible impacts in their communities, guide the implementation of management and monitoring efforts and provide the basis for planning as EAB continues to spread. Highlights ? High-resolution hyperspectral mapping of ash can exceed 80% accuracy in urban areas. ? However, ash detection accuracy decreases significantly in declining trees. ? In EAB infested areas mapping algorithms require the inclusion of vegetation indices.