Teja Kattenborn

Modeling forest biomass using very-high-resolution data - combining textural, spectral and photogrammetric predictors derived from spaceborne stereo images

Abstract We used spectral, textural and photogrammetric information from very-high resolution (VHR) stereo satellite data (Pléiades and WorldView-2) to estimate forest biomass across two test sites located in Chile and Germany. We compared Random Forest model performances of different predictor sets (spectral, textural, and photogrammetric), forest inventory designs and filter sizes (texture information). Best model performances were obtained with photogrammetric combined with either textural or spectral information and smaller, but more field plots. Stereo-VHR images showed a great potential for canopy height model (CHM) generation and could be an adequate alternative to LiDAR and InSAR techniques.

Segmentation of Forest to Tree Objects

This chapter reviews the use of airborne LiDAR data for the segmentation of forest to tree objects. The benefit obtained by LiDAR data is typically related to the use of the third dimension, i.e. the height data. Forest and stand objects may be segmented based on physical criteria, for example height and density information, while a further delineation to different timber types would require leaf-off data or an additional data source such as spectral images. Most forest applications of the LiDAR data are based on using digital surface models, but especially tree-level segmentation may benefit from a combination of raster and point data, and can be performed solely on point data. Finally, there are several established techniques for tree shape reconstruction based on the segmented point data.

Linking plant strategies and plant traits derived by radiative transfer modelling

Question Do spatial gradients of plant strategies correspond to patterns of plant traits obtained from a physically based model and hyperspectral imagery? It has been shown before that reflectance can be used to map plant strategies according to the established CSR scheme. So far, these approaches were based on empirical links and lacked transferability. Therefore, we test if physically-based derivations of plant traits may help in finding gradients in traits that are linked to strategies. Location A raised bog and minerotrophic fen complex, Murnauer Moos, Germany. Methods Spatial distributions of plant traits were modelled by adopting an inversion of the PROSAIL radiative transfer model on airborne hyperspectral imagery. The traits are derived from reflectance without making use of field data but only of known links between reflectance and traits. We tested whether previously found patterns in CSR plant strategies were related to the modelled traits. Results The results confirm close relationships between modelled plant traits and C, S and R strategies that were previously found in the field. The modelled plant traits explained different dimensions of the CSR-space. Leaf Area Index (LAI) and the reciprocal of Specific Leaf Area appeared to be good candidates for reproducing CSR scores as community traits using remote sensing. LAI has not been used in previous studies to allocate plant strategies. Conclusions Combining RTMs and the CSR model is a promising approach for establishing a robust link between airborne or spaceborne imagery and plant functioning. The demonstrated potential to map traits with close relation to CSR gradients using only our understanding of the relation between traits and reflectance is a step forward towards an operational use of the CSR model in remote sensing. This article is protected by copyright. All rights reserved.

Detecting the spread of invasive species in central Chile with a Sentinel-2 time-series

The presented work evaluates the potential of a Sentinel-2 time-series to detect Pinus radiata (Monterey Pine) invasions in endemic Nothofagus (Southern Beeches) forests in the Maule region, central Chile. Suitable cloud free images of the phenological cycle were selected from six Sentinel-2 scenes available for the years 2016. The scenes were unmixed using a non-negative least square (nnls) algorithm for different landcover components per pixel. The results were validated with a SVM classification of UAV-based hyperspectral mosaics acquired in March 2016. The results show that it is possible to map the coverage of the Pinus radiata class up to an accuracy of R2 ∼ 0.6 and RMSE of ∼ 10 %. Generally, a higher number of Sentinel-2 images increased the performance of the model, while there was no significant dependency on a specific acquisition date. However, the variability of the results is high, which indicates that a careful selection of multi-temporal endmembers is crucial to a successful unmixing of Pinus radiata.

Linking plant strategies (CSR) and remotely sensed plant traits

The widely established CSR-model quantitatively groups plants according to their ecological response, i.e. competitiveness, stress-toleration and ruderality. These plant strategies are allocated using plant traits. We assess the potential of canopy traits derived by imaging spectroscopy and inverted radiative transfer models for allocating CSR-scores. Our findings indicate that plant traits (LAI, Cab, Car and SLA) are valuable ‘soft traits’ to map plant strategies.