ACM Computing Surveys (CSUR)
Digital Photogrammetry
Advances in Computational Stereo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mapping the height and above-ground biomass of a mixed forest using lidar and stereo Ikonos images
International Journal of Remote Sensing - 3D Remote Sensing in Forestry
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Ranging techniques such as lidar (LIght Detection And Ranging) and digital stereo-photogrammetry show great promise for mapping forest canopy height. In this study, we combine these techniques to create hybrid photo-lidar canopy height models (CHMs). First, photogrammetric digital surface models (DSMs) created using automated stereo-matching were registered to corresponding lidar digital terrain models (DTMs). Photo-lidar CHMs were then produced by subtracting the lidar DTM from the photogrammetric DSM. This approach opens up the possibility of retrospective mapping of forest structure using archived aerial photographs. The main objective of the study was to evaluate the accuracy of photo-lidar CHMs by comparing them to reference lidar CHMs. The assessment revealed that stereo-matching parameters and left-right image dissimilarities caused by sunlight and viewing geometry have a significant influence on the quality of the photo DSMs. Our study showed that photo-lidar CHMs are well correlated to their lidar counterparts on a pixel-wise basis (r up to 0.89 in the best stereo-matching conditions), but have a lower resolution and accuracy. It also demonstrated that plot metrics extracted from the lidar and photo-lidar CHMs, such as height at the 95th percentile of 20 m×20 m windows, are highly correlated (r up to 0.95 in general matching conditions).