Schlund, Michael

[["dc.bibliographiccitation.firstpage","232"],["dc.bibliographiccitation.journal","ISPRS Journal of Photogrammetry and Remote Sensing"],["dc.bibliographiccitation.lastpage","241"],["dc.bibliographiccitation.volume","147"],["dc.contributor.author","Schlund, Michael"],["dc.contributor.author","Baron, Daniel"],["dc.contributor.author","Magdon, Paul"],["dc.contributor.author","Erasmi, Stefan"],["dc.date.accessioned","2018-12-18T11:04:32Z"],["dc.date.accessioned","2020-05-11T13:15:46Z"],["dc.date.available","2018-12-18T11:04:32Z"],["dc.date.available","2020-05-11T13:15:46Z"],["dc.date.issued","2019"],["dc.description.abstract","The potential of X-band interferometric synthetic aperture radar (InSAR) heights (e.g. from the TanDEM-X mission) for vegetation canopy height estimation has long been recognized. However, the penetration of the X-band into the canopy results in a height bias and substantially affects this estimation. The aim of the study was to apply a physical model to compensate the penetration depth (i.e. height bias) in canopy height estimation and evaluate its performance. We applied a penetration depth model on different TanDEM-X data in three German forests. This model is based on the volume coherence and imaging geometry of the InSAR acquisitions. We extracted the volume coherence from the TanDEM-X data and retrieved the height bias based on the penetration depth compared to actual surface heights. The modeled height bias was used to compensate the height bias in InSAR heights. The corrected TanDEM-X heights were evaluated with LiDAR data. In general, the penetration depth compensation in the InSAR heights improved the performance compared to the original InSAR heights resulting in elevations with a lower root mean squared error and the mean error decreased to less than 1m with the LiDAR heights used as reference. This suggested that the height bias was accurately modeled for temperate forests, which can be of high relevance when InSAR heights are used for canopy height estimation or used in multi-temporal analysis such as forest growth, degradation and deforestation monitoring."],["dc.identifier.doi","10.1016/j.isprsjprs.2018.11.021"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/65010"],["dc.language.iso","en"],["dc.notes.status","zu prüfen"],["dc.relation.issn","0924-2716"],["dc.title","Canopy penetration depth estimation with TanDEM-X and its compensation in temperate forests"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","101904"],["dc.bibliographiccitation.journal","International Journal of Applied Earth Observation and Geoinformation"],["dc.bibliographiccitation.volume","82"],["dc.contributor.author","Schlund, Michael"],["dc.contributor.author","Magdon, Paul"],["dc.contributor.author","Eaton, Brian"],["dc.contributor.author","Aumann, Craig"],["dc.contributor.author","Erasmi, Stefan"],["dc.date.accessioned","2020-05-12T08:30:55Z"],["dc.date.available","2020-05-12T08:30:55Z"],["dc.date.issued","2019"],["dc.description.abstract","Various semi-empirical models for linking PolInSAR data (polarimetric synthetic aperture radar interferometry) to canopy height of vegetation exist. However, only single-polarized data were used during the TanDEM-X mission in order to create a global digital elevation model (DEM). Therefore, simplifications of the semi-empirical models have to be applied to use the PolInSAR models for canopy height estimation with single-polarized TanDEM-X data. We extracted the volume coherence from TanDEM-X acquisitions and used a linear as well as a sinc model for the estimation of canopy height, which are based on the semi-empirical Random Volume over Ground model (RVoG). Both, the linear as well as the sinc model, were applied in temperate forests of Germany and boreal forests of Canada. The estimated canopy height was validated with LiDAR based canopy height models. In general, the sinc model resulted in higher coefficients of determination R2 from 0.08 to 0.64 and lower root mean squared errors (RMSE) between 4.8 m and 12.5 m compared to the linear model with R2 values between 0.08 and 0.62 (RMSE = 5.4 m to 13.5 m). Higher accuracies were generally achieved in winter and with higher height of ambiguity."],["dc.identifier.doi","10.1016/j.jag.2019.101904"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/65130"],["dc.language.iso","en"],["dc.relation.issn","0303-2434"],["dc.title","Canopy height estimation with TanDEM-X in temperate and boreal forests"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]