Nicole Pinnel

Sources of variance of downwelling irradiance in water

The downwelling irradiance in water is highly variable due to the focusing and defocusing of sunlight and skylight by the wave-modulated water surface. While the time scales and intensity variations caused by wave focusing are well studied, little is known about the induced spectral variability. Also, the impact of variations of sensor depth and inclination during the measurement on spectral irradiance has not been studied much. We have developed a model that relates the variance of spectral irradiance to the relevant parameters of the environmental and experimental conditions. A dataset from three German lakes was used to validate the model and to study the importance of each effect as a function of depth for the range of 0 to 5 m.

Method Analysis for Collecting and Processing in-situ Hyperspectral Needle Reflectance Data for Monitoring Norway Spruce

Forest damage induced by bark beetle attacks can cause major economic losses in forest- ry. Hyperspectral remote sensing data and state of the art very high spatial resolution satellite data of- fer a great potential for assessing tree vitality. How- ever, a better understanding of the effects of vitality decrease and its impact on the spectral behaviour of needles is needed. Filling this knowledge gap can make a significant contribution to improve the in- terpretation of remote sensing data. However, it is still unclear which method for needle spectra col- lection is most suitable. In this work, two methods for spectral reflectance measurements of Norway spruce needles using portable spectroradiometers were tested and analysed: using a classical fore op- tic and a so-called contact probe. The spectral re- flectance data were evaluated with different statis- tical similarity measure techniques. Besides ana- lysing the measurements themselves, the methods were compared in terms of their practicality. Fur- thermore, the impact of storage on the reflection behaviour was investigated. The spectral measure- ments were performed in the field as well as in a laboratory and repeated three times during the 2013 growing season. Based on the obtained results we recommend measuring needle samples with the contact probe of portable spectroradiometers.

The user interface of the EnMAP satellite mission

The Ground Segment for the future hyperspectral satellite mission EnMAP (Environmental Mapping and Analysis Program) will be designed, implemented and operated by the German Aerospace Center (DLR). The Applied Remote Sensing Cluster (DFD) at DLR is responsible for the establishment of a user interface. This paper provides first issues on design and functionality of the user interface. The user interface consists of two online portals. The EnMAP portal is the central entry point for all users interested to learn about the EnMAP mission, its objectives, status, and results. The EnMAP Data Access Portal (EDAP) provides a set of functions for registered users that will support the international EnMAP user community. The operational services offered through the EnMAP portal will be complemented by a service team, EnMAP Application Support, offering expert advice on the exploitation of EnMAP data.

Early Detection of Vitality Changes of Multi-Temporal Norway Spruce Laboratory Needle Measurements—The Ring-Barking Experiment

The focus of this analysis is on the early detection of forest health changes, specifically that of Norway spruce (Picea abies L. Karst.). In this analysis, we planned to examine the time (degree of early detection), spectral wavelengths and appropriate method for detecting vitality changes. To accomplish this, a ring-barking experiment with seven subsequent laboratory needle measurements was carried out in 2013 and 2014 in an area in southeastern Germany near Altotting. The experiment was also accompanied by visual crown condition assessment. In total, 140 spruce trees in groups of five were ring-barked with the same number of control trees in groups of five that were selected as reference trees in order to compare their development. The laboratory measurements were analysed regarding the separability of ring-barked and control samples using spectral reflectance, vegetation indices and derivative analysis. Subsequently, a random forest classifier for determining important spectral wavelength regions was applied. Results from the methods are consistent and showed a high importance of the visible (VIS) spectral region, very low importance of the near-infrared (NIR) and minor importance of the shortwave infrared (SWIR) spectral region. Using spectral reflectance data as well as indices, the earliest separation time was found to be 292 days after ring-barking. The derivative analysis showed that a significant separation was observed 152 days after ring-barking for six spectral features spread through VIS and SWIR. A significant separation was detected using a random forest classifier 292 days after ring-barking with 58% separability. The visual crown condition assessment was analysed regarding obvious changes of vitality and the first indication was observed 302 days after ring-barking as bark beetle infestation and yellowing of foliage in the ring-barked trees only. This experiment shows that an early detection, compared with visual crown assessment, is possible using the proposed methods for this specific data set. This study will contribute to ongoing research for early detection of vitality changes that will support foresters and decision makers.

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.