A key factor for operational forest management and forest monitoring is the availability of up-to-date spatial information on the state of forest resources. Earth observation can provide valuable contributions to these information needs. The German federal state of Rhineland-Palatinate transferred its inherited forest information system to a new architecture that is better able to serve the needs of centralized inventory and planning services, down to the level of forest districts. During this process, a spatially adaptive classification approach was developed to derive high-resolution forest information layers (e.g., forest type, tree species distribution, development stages) based on multi-temporal satellite data. This study covers the application of the developed approach to a regional scale (federal state level) and the further adaptation of the design to meet the information needs of the state forest service. The results confirm that the operational requirements for mapping accuracy can, in principle, be fulfilled. However, the state-wide mapping experiment also revealed that the ability
OPEN ACCESSForests 2015, 6 1983 to meet the required level of accuracy is largely dependent on the availability of satellite observations within the optimum phenological time-windows.
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.
Due to high variation in forest communities, forest structure and the fragmentation of the forested area in Central Europe, satellite-based forest inventory methods have to meet particularly high-quality requirements. This study presents an innovative method to combine official forest inventory information at stand level with multidate satellite imagery using a spatially adaptive classification approach for producing wall-to-wall forest cover maps of important tree species and management classes across multiple ownership regions in a heterogeneous low mountain range in Germany. The classification approach was applied to a 5,200-km 2 area (about 2,080 km 2 of forest land, mostly mixed forests) located in the Eifel mountain range in Central Europe. In comparison with conventional classifiers, our results demonstrate a significant increase in classification accuracy in the order of 12%. The method was tested with ASTER images but holds the potential to be used for regular state forest inventories based on standard and novel earth observation data supplied for instance from the SPOT-5 and RapidEye sensors.
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