Increases in extreme weather events associated with climate change have the potential to put currently healthy forests at risk. One option to minimize this risk is the application of forest management measures aimed at generating species mixtures predicted to be more resilient to these threats. In order to apply such measures appropriately, forest managers need up-to-date, accurate and consistent forest maps at relatively fine spatial resolutions. Cost efficiency is a major factor when creating such maps. Taking European spruce (Picea abies) and Scots pine (Pinus sylvestris) as an example, this paper describes an innovative approach for mapping two tree species using a combination of commercial very high resolution WorldView-2 (WV2) images and Landsat time series data. As a first step, this study used a supervised object-based classification of WV2 images covering relatively small test sites distributed across the region of interest. Using these classification maps as training data, wall-to-wall mapping of fractional coverages of spruce and pine was achieved using multi-temporal Landsat data and Random Forests (RF) regression. The method was applied for the entire state of Bavaria (Germany), which comprises a total forested area of approximately 26,000 km 2. As applied here, this two-step approach yields consistent and accurate maps of fractional tree cover estimates with a spatial resolution of 1 ha. Independent validation of the fractional cover estimates using 3780 reference samples collected through visual interpretation of orthophotos produced root-mean-square errors (RMSE) of 11% (for spruce) and 14% (for pine) with almost no bias, and R 2 values of 0.74 and 0.79 for spruce and pine, respectively. The majority of the validation samples (75% (spruce) and 84% (pine)) were modeled within the assumed uncertainty of ± 15% of the reference sample. Accuracies were significantly better compared to those achieved using a single-step classification of Landsat time series data at the pixel level (30 m), because the two-step approach better captures regional variation in the spectral signatures of target classes. Moreover, the increased number of available reference cells mitigates the impact of occasional errors in the reference data set. This two-step approach has great potential for cost-effective operational mapping of dominant forest types over large areas. 1. Introduction Climate change is expected to have an important influence on future growing conditions that will likely have a great impact on tree species. Depending on the location, some trees species will benefit, for example, from increasing temperatures and thus, extended vegetation periods. However, at other locations, increased temperatures are likely to lead to water stress, and hence, greater vulnerability of some tree species to abiotic and biotic disturbances (Lindner et al., 2010). In Central Europe, several coniferous tree species are of particular concern: the vulnerability of tree species such as European spruce and Scots pine is particularly hig...

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This study is a part of a research program that investigates the potential of RapidEye (RE) satellite data for timely updates of forest cover databases to reflect both regular management activities and sudden changes due to bark beetle and storms. Applied here in the Bavarian Forest National Park (BFNP) in southeastern Germany, this approach detected even small changes between two data takes, thus, facilitating documentation of regular management activities. In the case of a sudden event, forest cover databases also serve as a baseline for damage assessment. A storm event, which occurred on 13 July, 2011, provided the opportunity to assess the effectiveness of multi-seasonal RE data for rapid damage assessment. Images of sufficient quality (<20% cloud cover) acquired one day before the storm event were used as a baseline. Persistent cloud cover meant that the first "after event" image of sufficient quality was acquired six weeks later, on 22 August, 2011. Aerial images (AI) for the official damage assessment done by the BFNP administration were acquired on that same day. The RE analysis for damage assessment was completed two weeks after the post-event data take with an overall accuracy of 96% and a kappa coefficient of 0.86. In contrast, the official aerial image survey from the BFNP OPEN ACCESSForests 2014, 5 1285 was first released in late November, eleven weeks later. Comparison of the results from the two analyses showed a difference in the detected amount of forest cover loss of only 3%. The estimated cost of the RE approach was four times less than that of the standard digital AI procedure employed by the BFNP.

Nowadays, the image of the forest in Germany is changing from monoculture areas to very mixed forests, where individual stands are no longer clearly visible. The objective of this study was to examine the use of remotely sensed data at enterprise level for pre-stratification and sample plot allocation in the planning stage of forest inventories in a very heterogeneous forest. On the basis of RapidEye satellite data and object-based image analysis, a stratified segment-based non-permanent sampling design was developed and evaluated against the results of a permanent systematic sampling design. The relative efficiency (RE) was calculated based on variance estimators for simple random sampling and stratified random sampling for the variable timber volume [m 3 /ha]. By stratification of the sample designs, we achieved an RE of 1.25 for the systematic sampling and 1.34 with the segment-based sampling design. Based on a targeted standard error of 4.6%, the sampling designs were compared with respect to the required sample size. The stratified segment-based sampling design reduced the number of sample plots compared to the systematic sampling design by 28%. Furthermore, it was shown that the possible reduction of sampling plots leads to a cost saving of 21%.

Increased frequencies of storms and droughts due to climate change are changing central European forests more rapidly than in previous decades. To monitor these changes, multispectral 3D remote sensing (RS) data can provide relevant information for forest management and inventory. In this case study, data of the multispectral 3D-capable satellite system ZiYuan-3 (ZY-3) were used in a RS-guided forest inventory concept to reduce the field sample size compared to the standard grid inventory. We first pre-stratified the forest area via the ZY-3 dataset into coniferous, broadleaved and mixed forest types using object-based image analysis. Each forest type was then split into three height strata using the ZY-3 stereo module-derived digital canopy height model (CHM). Due to limited sample sizes, we reduced the nine to six strata. Then, for each of the six strata, we randomly selected representative segments for inventory plot placement. We then conducted field inventories in these plots. The collected field data were used to calculate forest attributes, such as tree species composition, timber volume and canopy height at plot level (terrestrially measured tree height and height information from ZY-3 CHM). Subsequently, we compared the resulting forest attributes from the RS-guided inventory with the reference data from a grid inventory based only on field plots. The difference in mean timber volumes to the reference was +30.21 m3ha−1 (8.99 per cent) for the RS-guided inventory with terrestrial height and −11.32 m3ha−1 (−3.37 per cent) with height information from ZY-3 data. The relative efficiency (RE) indicator was used to compare the different sampling schemes. The RE as compared to a random reduction of the sample size was 1.22 for the RS-guided inventory with terrestrial height measurements and 1.85 with height information from ZY-3 data. The results show that the presented workflow based on 3D ZY-3 data is suitable to support forest inventories by reducing the sample size and hence potentially increase the inventory frequency.