Given the importance of forest ecosystems, the availability of reliable, spatially explicit information about the site-specific climate sensitivity of tree species is essential for implementing suitable adaptation strategies. In this study, airborne hyperspectral data were used to assess the response of deciduous species (dominated by European beech and Sessile and Pedunculate oak) to water stress during a summery dry spell. After masking canopy gaps, shaded crown areas and non-deciduous species, potentially indicative spectral indices, the Photochemical Reflectance Index (PRI), Moisture Stress Index (MSI), Normalized Difference Water Index (NDWI), and Chlorophyll Index (CI), were analyzed with respect to available maps of site-specific soil moisture regimes. PRI provided an important indication of site-specific photosynthetic stress on leaf level in relation to limitations in soil water availability. The CI, MSI and NDWI revealed statistically significant differences in total chlorophyll and water concentration at the canopy level. However, after reducing the canopy effects by normalizing these indices with respect to the structure-sensitive simple ratio (SR) vegetation index, it was not yet possible to identify site-specific concentration differences in leaf level at this early stage of the drought. The selected indicators were also tested with simulated EnMAP and Sentinel-2 data (derived from the original airborne data set). While PRI proved to be useful also in the spatial resolution of EnMAP (GSD = 30 m), this was not the case with Sentinel-2, owing to the lack of adequate spectral bands; the remaining indicators (MSI, CI, SR) were also OPEN ACCESS Remote Sens. 2015, 7 14228 successfully produced with Sentinel-2 data at superior spatial resolution (GSD = 10 m). The study confirms the importance of using earth observation systems for supplementing traditional ecological site classification maps, particularly during dry spells and heat waves when ecological gradients are increasingly reflected in the spectral response at the tree crown level. It also underlined the importance of using Sentinel-2 and EnMAP in synergy, as soon as both systems become available.

This paper presents a rapid multi-return ALS-based (Airborne Laser Scanning) tree trunk detection approach. The multi-core Divide & Conquer algorithm uses a CBH (Crown Base Height) estimation and 3D-clustering approach to isolate points associated with single trunks. For each trunk, a principal-component-based linear model is fitted, while a deterministic modification of LO-RANSAC is used to identify an optimal model. The algorithm returns a vector-based model for each identified trunk while parameters like the ground position, zenith orientation, azimuth orientation and length of the trunk are provided. The algorithm performed well for a study area of 109 trees (about 2/3 Norway Spruce and 1/3 European Beech), with a point density of 7.6 points per m 2 , while a detection rate of about 75% and an overall accuracy of 84% were reached. Compared to crown-based tree detection methods, the aTrunk approach has the advantages of a high reliability (5% commission error) and its high tree positioning accuracy (0.59 m average difference and 0.78 m RMSE). The usage of overlapping segments with parametrizable size allows a seamless detection of the tree trunks.

The synergistic use of optical and SAR data for applications in agriculture and precision farming is analyzed. Plant parameters derived from optical sensors have proven to be very valuable inputs for accurate crop growth modeling and biomass monitoring. Information on the temporal development of Leaf Area Index (LAI) and structural canopy changes, such as harvest, strongly support the simulation of plant development and yield formation in a realistic and spatially distributed manner. The accuracy of LAI retrieval, based on RapidEye data using radiative transfer simulations with SLC, has been successfully validated with in-situ measurements for wheat, maize and rapeseed. Thus, LAI derived from optical data covering many fields and observing the whole crop cycle served for analyzing the sensitivity of TerraSAR-X to LAI for wheat. A high correlation between LAI values and radar backscatter especially in VV polarization was observed on field basis. Derivation of LAI from SAR-data can successfully complement LAI derived from optical data and thus stabilize the necessary data sources for plant modeling, making the data less dependent on weather conditions. Additionally, a structural indicator for the determination of the harvest date was found in the VH/VV backscatter ratio.Index Termsplant parameter retrieval, LAI, yield estimation, X-band radar, SLC

ABSTRACT:Dryness stress is expected to become a more common problem in central European forests due to the predicted regional climate change. Forest management has to adapt to climate change in time and think ahead several decades in decisions on which tree species to plant at which locations. The summer of 2003 was the most severe dryness event in recent time, but more periods like this are expected. Since forests on different sites react quite differently to drought conditions, we used the process-based growth model BiomeBGC and climate time series from sites all over Germany to simulate the reaction of deciduous and coniferous tree stands in different characteristics of drought stress. Times with exceptionally high values of water vapour pressure deficit coincided with negative modelled values of net primary production (NPP). In addition, in these warmest periods the usually positive relationship between temperature and NPP was inversed, i.e., under stress conditions, more sunlight does not lead to more photosynthesis but to stomatal closure and reduced productivity. Thus we took negative NPP as an indicator for drought stress. In most regions, 2003 was the year with the most intense stress, but the results were quite variable regionally. We used the Modis MOD17 gross and net primary production product time series and MOD12 land cover classification to validate the spatial patterns observed in the model runs and found good agreement between modelled and observed behaviour. Thus, BiomeBGC simulations with realistic site parameterization and climate data in combination with species-and variety-specific ecophysiological constants can be used to assist in decisions on which trees to plant on a given site.

The Danube Basin has been hit by several droughts in the last few years. As climate change makes weather extremes and temperature records in late winter and early spring more likely, water availability and irrigation possibilities become more important. In this paper, the crop water demand at field and national scale within the Danube Basin is presented using a dense time series of multispectral Sentinel-2 data, for crop type maps derived with deep learning techniques and physically-based models for crop parameter retrieval and crop growth modelling.