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The landscape-human relationships on the Alps, the more populated mountain region globally, depend on tree species diversity, their canopy height and canopy gaps (soil cover). The monitoring of such forest information plays an important role in forest management planning and therefore in the definition of present and future mountain forest services. In order to gain wide scale and high-resolution forest information, very high-resolution (VHR) stereo satellite imagery has the main benefit of covering large areas with short repetition intervals. However, the application of this technology is not fully assessed in terms of accuracy in dynamic year-around forest conditions. In this study, we investigate on four study sites in the Swiss Alps 1) the accuracy of forest metrics in the Alpine forests derived from VHR Pleiades satellite images and 2) the relation of associated errors with shadows, terrain aspect and slope, and forest characteristics. We outline a grid-based approach to derive the main forest metrics (descriptive statistics) from the canopy height models (CHMs) such as the maximum height (Hmax), height percentiles (Hp95, Hp50), the standard deviation of the height values (HStd) and canopy gaps. The Pleiades-based forest metrics are compared with those obtained by aerial image matching, a technology operationally used for deriving this information. For the study site with aerial and satellite images acquired almost at the same time, this comparison shows that the medians of Pleiades forest metrics error are -0.25 m (Hmax), 0.33 m (Hp95), −0.03 m (HStd) and -5.6% for the canopy gaps. The highest correlation (R2 = 0.74) between Pleiades and aerial canopy gaps is found for very bright areas. Conversely, in shadowed forested areas a R2 of only 0.16 is obtained. In forested areas with steep terrain (>50°), Pleiades forest metrics show high variance for all the study areas. Concerning the canopy gaps in these areas, the correlation between Pleiades and the reference data provides a correlation value of R2 = 0.20, whereas R2 increases to 0.66 for gently sloped areas (10-20°). The aspect does not provide a significant correlation with the accuracy of the Pleiades forest metrics. However, the extended shadowed mainly on north/northwest facing slopes caused by trees or terrain shade negatively affect the performance of stereo dense image matching, and hence the forests metrics. The occurrence of strong shadows in the forested areas increases dramatically by ˜40% in the winter season due to the lower sun elevation. Furthermore, due to the leaf-off condition in the winter season dense image matching may fail to derive the canopy heights. Our results show that Pleiades CHMs could be a useful alternative to CHMs based on aerial images matching for monitoring forest metrics and canopy gaps in mountain forests if captured during leaf-on conditions. Our study offers forest research, as well as forest management planning, the benefit of a better understanding of the performance of VHR satellite imagery used for forest inventory in mountainous regions and in similar forest environments.

The continual increase of shrub forest in the Swiss Alps over the past few decades impacts biodiversity, forest succession and the protective function of forests. Therefore, up-to-date and area-wide information on its distribution is of great interest. To detect the shrub forest areas for the whole of Switzerland (41,285 km2), we developed an approach that uses Random Forest (RF), bias correction techniques and data from multiple remote sensing sources. Manual aerial orthoimage interpretation of shrub forest areas was conducted in a non-probabilistic way to derive initial training data. The multi-sensor and open access predictor data included digital terrain and vegetation height models obtained from Airborne Laser Scanning (ALS) and stereo-imagery, as well as Synthetic Aperture Radar (SAR) backscatter from Sentinel-1 and multispectral imagery from Sentinel-2. To mitigate the expected bias due to the training data sampling strategy, two techniques using RF probability estimates were tested to improve mapping accuracy. 1) an iterative and semi-automated active learning technique was used to generate further training data and 2) threshold-moving related object growing was applied. Both techniques facilitated the production of a shrub forest map for the whole of Switzerland at a spatial resolution of 10 m. An accuracy assessment was performed using independent data covering 7640 regularly distributed National Forest Inventory (NFI) plots. We observed the influence of the bias correction techniques and found higher accuracies after each performed iteration. The Mean Absolute Error (MAE) for the predicted shrub forest proportion was reduced from 6.04% to 2.68% while achieving a Mean Bias Error (MBE) of close to 0. The present study underscores the potential of combining multi-sensor data with bias correction techniques to provide cost-effective and accurate countrywide detection of shrub forest. Moreover, the map complements currently available NFI plot sample point data.

Although trees growing outside forests are less perceived in relation to those inside forests, they are also valuable and serve various functions. The biophysical thresholds of Trees Outside Forests (TOF) were recently defined and standardised by the Global Forest Resources Assessments of the United Nations Food and Agricultural Organization (UNFAO-FRA). With these definitions, standardised mapping of TOF resources became feasible at the national and international level. Here, we aimed to fill the spatial information gap of TOF resources at the national scale with an automated mapping approach based on the UNFAO-FRA definition. The approach was carried out in ArcGIS using adapted UNFAO-FRA biophysical thresholds with routinely acquired countrywide remote sensing data for the whole of Switzerland: a Vegetation Height Model (VHM), a Topographic Landscape Model (TLM Regio) land cover map, and a Forest Mask. Results were validated using stereo-image interpretation data of the Swiss National Forest Inventory (NFI), which verified 95%, 55% and 75% of overall, producer’s and user’s accuracy, respectively. Of the five forest production regions in Switzerland, the highest accuracy was achieved in the Central Plateau and the lowest in the Alps, with accuracy generally decreasing with increasing elevation. Omission and commission errors were highly correlated with the vertical and horizontal accuracy of the VHM, and the applied biophysical thresholds caused both types of error. The final TOF map produced with our approach is at the countrywide scale, is superior to existing TOF information, meets UNFAO-FRA management and reporting needs, and enables the derivation of TOF’s biomass, carbon sequestration potential and species distribution for the whole country.

The demand for precise mapping and monitoring of forest resources, such as above ground biomass (AGB), has increased rapidly. National accounting and monitoring of AGB requires regularly updated information based on consistent methods. While remote sensing technologies such as airborne laser scanning (ALS) and digital aerial photogrammetry (DAP) have been shown to deliver the necessary 3D spatial data for AGB mapping, the capacity of repeat acquisition, remotely sensed, vegetation structure data for AGB monitoring has received less attention. Here, we use vegetation height models (VHMs) derived from repeat acquisition DAP data (with ALS terrain correction) to map and monitor woody AGB dynamics across Switzerland over 35 years (1983-2017 inclusive), using a linear least-squares regression approach. We demonstrate a consistent relationship between canopy height derived from DAP and field-based NFI measures of woody AGB across four inventory periods. Over the environmentally heterogeneous area of Switzerland, our models have a comparable predictive performance (R2 = 0.54) to previous work predicting AGB based on ALS metrics. Pearson correlation coefficients between measured and predicted changes in woody AGB over time increased with shorter time gaps (