Stefanie Holzwarth

Incorporating a push-broom scanner into a generic hyperspectral processing chain

DLR is operating a generic processing chain for imaging spectrometer data. This includes automatic invocation of system correction, parametric geocoding and atmospheric correction with accompanying quality measurements as well as the archiving of the resulting data products. Further HySpex, a pushbroom scanner, has been purchased in 2011 to be operated from spring 2012. This work describes the steps to be accomplished to incorporate this new sensor into the generic environment.

FEATURE-BASED TREE SPECIES CLASSIFICATION USING HYPERSPECTRAL AND LIDAR DATA IN THE BAVARIAN FOREST NATIONAL PARK

The Bavarian Forest National Park, established in 1970, is a unique area of forests with large nonintervention zones, which promote a large-scale rewilding process with low human interference. Thus, the National Park authority is particularly interested in investigating the structure and dynamics of the forest ecosystems within the park. However, conventional forest inventories are timeconsuming and not able to fully record the heterogeneity of natural forests. Our goal is to develop advanced techniques for tree species mapping based on hyperspectral remote sensing in combination with other remote sensing and in situ measurements that meet the demands of the National Park. This approach needs to be adapted to the heterogeneous appearance of the forest. This work aims at building a model transferable to an area-wide mapping of tree species based on the needs of the Bavarian Forest National Park. It reveals the requirements for tree species mapping and shows which spectral/spatial features and data combinations generate the best results within a Random Forest modelling approach. The study is based on airborne hyperspectral data acquired with the HySpex VNIR-1600 sensor (160 spectral bands, 400 – 990 nm, 1.6 m spatial resolution). Additional full waveform LiDAR data, including a Digital Surface Model, Digital Terrain Model and a Digital Canopy Height Model, were available for the analysis. Individual tree crowns as well as clusters of tree crowns from 13 different tree species were located and identified during a field survey. The field-demarcated tree canopies were used as reference data for creating the feature database. Several preprocessing steps including atmospheric correction, spectral and spatial polishing, bidirectional reflectance distribution function (BRDF) effect correction as well as ortho-rectification of the hyperspectral imagery were conducted before the analysis. A band selection procedure based on principal component analysis, band correlation, and band variance was performed to identify the most appropriate spectral bands for species discrimination, resulting in a set of 53 spectral bands. Seven different combinations of hyperspectral, structural and terrain-specific parameters contained in the feature database were investigated in a Random Forest Modelling approach to ascertain which variables enhance the overall classification accuracy. A classification model using all available parameters in the feature database yielded an overall accuracy that is 17 percentage points higher (94%) compared to using only the preselected spectral bands (77%). For most of the 13 tree species, the final classification model achieved individual class accuracies of more than 90%. The study showed that a tree species feature database consisting of hyperspectral signatures and relatively simple LiDAR derived features has high potential for a forest inventory based on remote

Tree species classification using plant functional traits from LiDAR and hyperspectral data

Abstract Plant functional traits have been extensively used to describe, rank and discriminate species according to their variability between species in classical plant taxonomy. However, the utility of plant functional traits for tree species classification from remote sensing data in natural forests has not been clearly established. In this study, we integrated three selected plant functional traits (i.e. equivalent water thickness (Cw), leaf mass per area (Cm) and leaf chlorophyll (Cab)) retrieved from hyperspectral data with hyperspectral derived spectral features and airborne LiDAR derived metrics for mapping five tree species in a natural forest in Germany. Our results showed that when plant functional traits were combined with spectral features and LiDAR metrics, an overall accuracy of 83.7% was obtained, which was statistically significantly higher than using LiDAR (65.1%) or hyperspectral (69.3%) data alone. The results of our study demonstrate that plant functional traits retrieved from hyperspectral data using radiative transfer models can be used in conjunction with hyperspectral features and LiDAR metrics to further improve individual tree species classification in a mixed temperate forest.

Report on International Spaceborne Imaging Spectroscopy Technical Committee calibration and validation workshop, national environment research council field spectroscopy facility, University of Edinburgh

Calibration and validation are fundamental for obtaining quantitative information from Earth Observation (EO) sensor data. Recognising this and the impending launch of at least five sensors in the next five years, the International Spaceborne Imaging Spectroscopy Technical Committee instigated a calibration and validation initiative. A workshop was conducted recently as part of this initiative with the objective of establishing a good practice framework for radiometric and spectral calibration and validation in support of spaceborne imaging spectroscopy missions. This paper presents the outcomes and recommendations for future work arising from the workshop.