Impact of multiangular information on empirical models to estimate canopy nitrogen concentration in mixed forest

Directional effects in remotely sensed reflectance data can influence the retrieval of plant biophysical and biochemical estimates. Previous studies have demonstrated that directional measurements contain added information that may increase the accuracy of estimated plant structural parameters. Because accurate biochemistry mapping is linked to vegetation structure, also models to estimate canopy nitrogen concentration (CN) may be improved indirectly from using multiangular data. Hyperspectral imagery with five different viewing zenith angles was acquired by the spaceborne CHRIS sensor over a forest study site in Switzerland. Fifteen canopy reflectance spectra corresponding to subplots of field-sampled trees were extracted from the preprocessed CHRIS images and subsequently two-term models were developed by regressing CN on four datasets comprising either original or continuum-removed reflectances. Consideration is given to the directional sensitivity of the CN estimation by generating regression models based on various combinations (n=15) of observation angles. The results of this study show that estimating canopy CN with only nadir data is not optimal irrespective of spectral data processing. Moreover adding multiangular information improves significantly the regression model fits and thus the retrieval of forest canopy biochemistry. These findings support the potential of multiangular Earth observations also for application-oriented ecological monitoring

Spatial resolution, spectral metrics and biomass are key aspects in estimating plant species richness from spectral diversity in species‐rich grasslands

Increasing evidence suggests that remotely sensed spectral diversity is linked to plant species richness. However, a conflicting spectral diversity–biodiversity relationship in grasslands has been found in previous studies. In particular, it remains unclear how well the spectral diversity–biodiversity relationship holds in naturally assembled species-rich grasslands. To address the linkage between spectral diversity and plant species richness in a species-rich alpine grassland ecosystem, we investigated (i) the trade-off between spectral and spatial resolution in remote sensing data; (ii) the suitability of three different spectral metrics to describe spectral diversity (coefficient of variation, convex hull volume and spectral species richness) and (iii) the importance of confounding effects of live plant biomass, dead plant biomass and plant life forms on the spectral diversity–biodiversity relationship. We addressed these questions using remote sensing data collected with consumer-grade cameras with four spectral bands and 10 cm spatial resolution on an unmanned aerial vehicle (UAV), airborne imaging spectrometer data (AVIRIS-NG) with 372 bands and 2.5 m spatial resolution, and a fused data product of both datasets. Our findings suggest that a fused dataset can cope with the requirement of both high spatial- and spectral resolution to remotely measure biodiversity. However, in contrast to several previous studies, we found a negative correlation between plant species richness and spectral metrics based on the spectral information content (i.e. spectral complexity). The spectral diversity calculated based on the spectral complexity was sensitive to live and dead plant biomass. Overall, our results suggest that remote sensing of plant species diversity requires a high spatial resolution, the use of classification-based spectral metrics, such as spectral species richness, and awareness of confounding factors (e.g. plant biomass), which may be ecosystem specific

Seasonal study of directional reflectance properties of snow

We present an analysis of the hemispherical-directional reflectance factor (HDRF) of snow, using 16 seasonal datasets of the spectral range from 400 to 2,500 nm. The data was measured under clear sky conditions in Davos Dorf (Grisons, Switzerland, 1,560 m a. s. l.). Fieldwork was carried out on seven days between February 5 and March 30 2004 with the Swiss Field Goniometer System (FIGOS). In addition to the HDRF measurements, snow stratigraphy, temperature and density were measured, and chemical and photomicroscopical analyses of snow samples were performed. Concentration of organic and elemental carbon was determined by chemical analysis. The grain size analyses through image processing of micrographs revealed relatively small differences of 0.21 to 0.33 mm mean radius in the top layers of snow cover. Seven datasets present HDRF of wet snow surfaces with similar anisotropy at smaller sun zenith angles (qI = 3.3 to 64.5°) compared to the nine surfaces measured at larger sun zenith angles (qI = 6.5 to 75.3°). Spectral albedo is relatively constant throughout datasets of different sun zenith angles of the same day, but has large variability among measurements of different days. With increasing wavelength, it decreases significantly faster for wet surfaces than for dry surfaces. The forward scattering peak was found to strengthen with increasing sun zenith angle and with increasing wavelength for both wet and dry surfaces at wavelengths above 700 nm. Finally, a spectral wet snow determination method is performed and the cross-sensitivity to HDRF variation could be derived. The best differentiability was found for 1,380 nm. This basis work increases the understanding of snow signatures for potential imaging spectroscopy applications in alpine regions

Design and prototyping of the SPECTRA simulator architecture

SPECTRA (Surface Processes and Ecosystem Changes through Response Analysis) is a planned spaceborne multiangular hyperspectral and thermal imaging spectrometer in phase A early design led by ESA's earth observation group. Its mission is to describe, understand and model the role of terrestrial vegetation in the global carbon cycle and its response to climate variability. Even though the project has been terminated in November 2005, many results of the phase A studies are considered to be useful as input to future missions. The SPECTRA end-to-end simulator is intended to be used to test different aspects of the SPECTRA mission concept and for tuning the retrieval algorithms as well as assessing their performances. The intention of this ESA-commissioned study was not to build an actually working simulator, but to conceive an architecture for a simulator to be built during phase B of the SPECTRA design, as well as perform a limited validation of this architecture. The software architecture for the future SPECTRA end-to-end simulator has been designed to be modular, flexible and distributed. It consists of a central control unit with associated database, which is controlled and monitored via an internet-accessible web interface, and a flexible number of modules performing the actual calculations. The list of simulator modules currently includes but is not limited to state-of-the-art developments in radiative transfer (Onera), instrument modelling (ESA), atmospheric correction (Onera), and various level 2 algorithms (Alterra). Assimilation models and global carbon flux models are linked to the simulator via the SPECTRA field segment database (RSL and Princeton), for which a high level schema has been defined. The simulator structure has been validated using full end-to-end simulations from ground data to top-of-atmosphere, through the SPECTRA instrument simulator provided by industry, and back again. Test data from the Barrax field site are used for this purpose (University of Valencia)

The spectral database SPECCHIO in support of Cal/Val activities

Field spectroscopy is a fundamental to Cal/Val as it provides a baseline for satellite and airborne measurements. The considerable time and money spent on the collection of accurate and valuable spectral ground reference data calls for a storage approach that maximises the utilisation of these data by data sharing while ensuring long-term usability. In this paper we present the state of the art of spectral databases on the example of the SPECCHIO database, its application to Cal/Val activities within the framework of the APEX (Airborne Prism EXperiment) project and general conclusions for future capabilities of spectral repositories

Mapping of Welwitschia Mirabilis with high resolution satellite imagery in the Namib Desert

This study is about a non-invasive mapping technique of Welwitschia mirabilis in its habitat, the Namib-Naukluft Park, based on spatially high resolved satellite images. First, two satellite data sets from IKONOS and Quickbird satelliteare geometrically corrected, including GPS ground control points and a digital elevation model. Second, Welwitschia individuals, bushes, sandy and rocky surroundings are successfully mapped with a supervised and object-oriented classification approach. GPS points, training samples and verification objects have been collected previously in a field survey. 49 of 55 (89%) Welwitschia individuals known in ground reference, were classified successfully in both data sets, despite the limited spectral and spatial resolution. Compared to a pixel-based classification of Welwitschia with IKONOS data also published on this conference, it was shown, that the object-oriented approach improves the mapping precision of Welwitschia species. The presented area wide mapping technique from space is effective and non-invasive, but limited by the spatial and spectral resolution of the input satellite data

SPECCHIO: a free spectral data management and processing system

The management and storage of spectroradiometer data are important issues, especially in regards of long-term use, data quality and shareability. The SPECCHIO spectral database system developed at the Remote Sensing Laboratories (RSL) provides a solution for the organized storage of spectral data and associated metadata and for the spectral processing based on interactive, customizable and generic processing chains. Optimized data structures and graphical user interfaces combined with intelligent file parsing routines enable the efficient entry of spectral data and metadata. The system can be operated in a heterogeneous computing environment, offering multiuser access to a centralized database and enabling easy data sharing within and even across research groups

Brandgutdifferenzierung in einem Wildland-Urban Interface mit Hilfe von Laser Scanning und Bildspektrometrie

Zusammenfassung: Im Bereich von Waldbränden stellt die Kartierung von Brandgut zur Minderung von Risiken eine große Herausforderung dar. Besonders schwierig gestaltet sich das Unterfangen, wenn anthropogene und natürliche Strukturen aufeinander treffen und ein sogenanntes Wildland-Urban Interface bilden. Ein support vector machines-Algorithmus wird trainiert, um eine Landbedeckungskartierung aus einem kombinierten LiDAR- und Bildspektrometerdatensatz vorzunehmen. Es werden 18 Klassen unterschieden, wobei die Vegetation in sechs Brandguttypen eingeteilt wird. Sechs Prozessierungsketten mit unterschiedlichen Hierarchien und Fusionszeitpunkten werden untersucht. Die erreichten Gesamtgenauigkeiten liegen zwischen 53.07% und 70.69%, bzw. 0.48 und 0.68 Kappa. Diese Ergebnisse werden den Klassifikationen der einzelnen Sensorquellen gegenüber gestellt. Die maximal erreichte Verbesserung durch die Fusion beträgt 18.96% bzw. 0.19 Kappa