Search

For the provision of various ecosystem services in steep terrain, such as protection against natural hazards, a forest must be managed, which often requires the use of cable yarders. The design of a cable road is a complex and demanding task that also includes the search for appropriate support and anchor trees. The aim of this study was to evaluate whether and with what reliability potential support trees for cable yarding can be detected using remote sensing data. The detection of potential support trees was tested using 48 method combinations on 10 test plots of the Experimental Forest Management project in cable yarder terrain in the Swiss Alps in the Canton of Grisons. The most suitable method combinations used a Gaussian filter and a local maxima algorithm. On average, they had an extraction rate of 108.9–124.5% (root mean square, RMS) and a mean commission error of 66.0–67.2% (RMS). The correctly detected trees deviated horizontally by an average of 1.8 to 1.9 m from the position of the reference trees. The difference in tree heights was 1.1 to 1.6 m. However, for the application of single tree detection to support cable road planning in steep and complex terrain, too few potential support trees were detected. Nonetheless, the accuracy of the extracted tree parameters would already be sufficient for cable road planning.

Waldstrukturen lassen sich mit Methoden der Fernerkundung über grosse Gebiete erfassen. Dabei haben sich Stereoluftbilder als effiziente Grundlage für die Berechnung von digitalen Oberflächenmodellen erwiesen. Zieht man von diesen Oberflächenmodellen das Geländemodell ab und eliminiert künstliche Objekte, erhält man Vegetationshöhenmodelle. Je nach Auflösung der Luftbilder liefern diese in den Vegetationshöhen mehr oder weniger detaillierte Informationen über die Kronenoberflächen. Um Daten zu früheren Vegetationshöhen zu erhalten, wurden im Schweizerischen Landesforstinventar (LFI) auf Basis analoger Schwarz-Weiss-Luftbilder des Bundesamts für Landestopografie aus den Befliegungskampagnen 1979–1985 und 1990–1998 landesweite digitale Oberflächen- und Vegetationshöhenmodelle erstellt. Anschliessend wurden mit den historischen Vegetationshöhenmodellen (Zeitschnitte 1980 und 1990) und dem aktuellen Vegetationshöhenmodell des LFI (Zeitschnitt 2010) die Höhenveränderungen im Schweizer Wald in einer Auflösung von einer Hektare bestimmt. Anders als im Offenland und im Siedlungsgebiet funktionierte die Bildkorrelation im Wald mit den gescannten Schwarz-Weiss-Luftbildern zufriedenstellend. Über eine Million Hektaren Wald konnten in die Analyse einbezogen werden. Sie zeigt, dass die Vegetationshöhe auf fast einem Drittel der Waldfläche seit den 1980er-Jahren kontinuierlich zugenommen hat. Auf einer nahezu gleich grossen Fläche waren die Höhenveränderungen zwischen den drei Zeitschnitten gegenläufig mit Zunahmen in der einen und Abnahmen in der anderen Periode. Die grössten Höhenveränderungen waren im Mittelland zu verzeichnen, die geringsten auf der Alpensüdseite. Kleinräumige Veränderungen und Änderungen unter dem Kronendach können mit diesen Daten allerdings nicht erkannt werden. Trotzdem erlauben die historischen Vegetationshöhenmodelle einen Blick in die Vergangenheit der Schweizer Waldstrukturen.

Kurzes Zeitfenster für die Fichtennaturverjüngung in Gebirgsnadelwäldern In den vergangenen 30 Jahren wurden Schweizer Gebirgsnadelwälder stellenweise mit Kahlhieben verjüngt und nicht mit kleinen Lücken, wie dies bei der Gebirgsplenterung üblich ist. Wie sich die Entwicklung in den entstandenen Bestandeslücken nach 13 bis 29 Jahren präsentiert, wurde in zehn Kahlhieben in der Südostschweiz und im Tessin mit Luftbildanalysen und Stichprobeninventuren untersucht. Historische Luftbilder dienten zur Erfassung der Bestandesgeschichte und der Kronendachöffnung vor dem Schlag. Mit Stichprobeninventuren wurden die aktuelle Baumverjüngung und die Kleinstandorte inklusive Bodenbedeckung erhoben. Die Dichte der Verjüngung zwischen 10 cm Höhe und 12 cm Brusthöhendurchmesser (BHD) lag im Mittel bei 5450 St./ha, wovon 71% Fichten waren. Der hohe Mittelwert kam durch Probeflächen mit sehr dichter Verjüngung zustande. Die Fichtenverjüngung stellte sich zum Grossteil nach dem Holzschlag ein; Vorverjüngung war im Nachwuchs (130 cm Höhe bis 12 cm BHD) zu 30% vorhanden. Die Vorverjüngung war dichter in Probeflächen, in denen der Vorbestand schon aufgelichtet gewesen war. Die höchsten Dichten der Nachverjüngung traten bei mittlerer bis hoher Kronendeckung vor dem Holzschlag auf. Verdämmende Konkurrenzvegetation reduzierte die Dichte der Vor- und der Nachverjüngung. Die Präsenz von Moderholz und Baumstümpfen war mit einer höheren Verjüngungsdichte verbunden, Wildhuftierverbiss dagegen mit einer geringeren. Bei wenig Konkurrenzvegetation tritt unmittelbar nach dem Schlagen einer grossen Lücke eine Phase mit hoher Verjüngungsgunst auf, in der die Verjüngung oft gelingt. Auf Standorten hingegen, die zur Bildung einer dichten Konkurrenzvegetation neigen, stellt sich die Verjüngung nur zögerlich ein, und sie ist auf Moderholz und Baumstümpfe angewiesen.

Räumliche Analyse von Trockenheitssymptomen im Schweizer Wald mit Sentinel-2-Satellitendaten Andri Baltensweiler, Philipp Brun, Joanna Pranga, Achilleas Psomas, Niklaus E. Zimmermann, Christian Ginzler Eidgenössische Forschungsanstalt für Wald, Schnee und Landschaft, WSL (CH)*, Eidgenössische Forschungsanstalt für Wald, Schnee und Landschaft, WSL (CH) *Zürcherstrasse 111, CH-8903 Birmensdorf, E-Mail andri.baltensweiler@wsl.ch, Eidgenössische Forschungsanstalt für Wald, Schnee und Landschaft, WSL (CH), Eidgenössische Forschungsanstalt für Wald, Schnee und Landschaft, WSL (CH), Eidgenössische Forschungsanstalt für Wald, Schnee und Landschaft, WSL (CH), Eidgenössische Forschungsanstalt für Wald, Schnee und Landschaft, WSL (CH) Multispektrale Satellitenbilder in hoher zeitlicher Wiederholung wurden genutzt, um die Gebiete in der Schweiz zu ermitteln, in denen die Buchen, aber auch die Nadelbäume besonders stark von der im Sommer 2018 herrschenden Trockenheit betroffen waren. Hierfür wurden aus den Satellitenbildern Vegetationsindizes berechnet und Schwellenwerte für Trockenheitssymptome wie die vorzeitige Verbraunung abgeleitet. Durch die freie Verfügbarkeit der Sentinel-2-Daten des Copernicus-Programms und von Cloud-Plattformen wie Google Earth Engine konnten die Analysen rasch durchgeführt werden.

Forest dynamics and shallow landslides: a large-scale GIS-analysis Through continuous and abrupt changes in the forest and particularly in its root area, the disposition for shallow landslides also changes. The aim of this article is to learn more about the relationships between long-term forest dynamics and the risk of landslides with the help of large spatial data sets. A total of 6670 shallow landslides were selected from the natural event register StorMe, the event register of the Canton of Obwalden and the WSL database for shallow landslides and slope debris flows and overlaid with GIS data on the current and former forest situation (Siegfried maps of approx. 1880) and with windthrow areas (Vivian and Lothar). Shallow landslides occurred less frequently in the forest than outside of it at inclinations of less than 35 degrees. Higher landslide densities occurred in coniferous and mixed forests (compared to deciduous forests) and in relatively young forests (ingrown after 1880). Landslides in younger forests occurred on slopes less steep than in older forests. More shallow landslides occurred on former windthrow areas during the first 3 to 17 years after the event, however these landslides occurred on comparatively steep slopes. The analyses show that the disposition towards shallow landslides defined by slope inclination and subsoil can change strongly due to reforestation processes and natural disturbances. Silvicultural interventions may contribute to an improvement of the protective effect in the long term if they are carried out carefully and if they contribute to an increased adaptability to natural disturbances.

Efficient forest operations are required for the provision of biodiversity and numerous ecosystem services, such as wood production, carbon sequestration, protection against natural hazards and recreation. In numerous countries, under difficult terrain conditions, the costs of forest management and harvesting are not covered by timber revenue. One possible option to increase the cost-effectiveness of the forestry sector is the application of state-of-the-art harvesting and extraction techniques, so-called best suitable harvesting methods. We present a case study from Switzerland, where a lack of competitiveness in the forestry sector is of particular interest, with the aim of quantifying the efficiency gains if estimated best suitable harvesting methods were to be rigorously applied instead of the currently applied harvesting methods. For this purpose, we developed a spatial decision support system to allocate estimated best suitable harvesting methods to plots, while concurrently considering hauling route limitations, extraction route properties and stand characteristics. Our approach was based on productivity models and supported with expert-defined decision trees. The evaluation of the estimated best suitable harvesting methods and the comparison with the currently applied harvesting methods were completed for all 6500 National Forest Inventory (NFI) plots in Switzerland. We draw the following three major conclusions from our study: First, our modeling approach is an effective method to allocate estimated best suitable harvesting methods to NFI plots. Second, applying estimated best suitable harvesting methods would lead to cost reductions, in particular in the regions that include steep terrain and where harvesting mainly relies on cable- and air based extraction methods. Third, assuming an average timber price of 75 CHF m -3, 64 % instead of 52 % of the forest area could be harvested economically over the whole country if estimated best suitable methods were applied. This advantage would mainly be caused by a shift towards more mechanized harvesting methods. Improving the cost-effectiveness of the forestry sector is of high global relevance, as the increased use of domestic timber resources is a cost-efficient way to reduce atmospheric carbon emissions. The methodological framework described here was developed for Switzerland in particular, but it could be applied to Central Europe and other parts of Europe with a large amount of mountain forests.

Grossflachige Klassifikation von Gebuschwald mit Fernerkundungsdaten Informationen uber die Ausdehnung und die Entwicklung von Gebuschwaldern sind fur verschiedene forstliche und okologische Fragestellungen von Bedeutung, jedoch sind aktuelle und flachendeckende Datensatze kaum verfugbar. Im Rahmen dieser Arbeit wurde die Kartierung des Gebuschwaldes mit Dominanz von Alpenerle, Legfohre und Hasel fur den Kanton Graubunden auf Basis von landesweit verfugbaren Fernerkundungsdaten untersucht. Es wurden die Satellitendaten von Sentinel-1 und Sentinel-2 sowie ein Vegetationshohenmodell und ein Gelandemodell verwendet. Fur die uberwachte Klassifikation mit Random Forest, einem auf Entscheidungsbaumen basierenden Verfahren des maschinellen Lernens, wurden vom Kanton bereitgestellte und durch Luftbildinterpretation erganzte Trainingsflachen verwendet. Die unabhangige Validierung der Ergebnisse wurde mit Daten des Landesforstinventars (LFI) durchgefuhrt. Alpenerlen- und Legfohrenwalder liessen sich mit einer hohen Genauigkeit von 92.1% bzw. 86.7% klassifizieren, fur Haselwalder betrug die interne Modellgute hingegen nur 66.7%. Die resultierende Flachenausdehnung des Gebuschwaldes war vergleichbar mit den Angaben des LFI. Beim direkten Vergleich mit den LFI-Luftbildinterpretationspunkten wurden grossere Abweichungen festgestellt. Der Hauptgrund dafur ist der unterschiedliche raumliche Detaillierungsgrad. LFI-Flachen mit einem hohen Strauchanteil wurden jedoch zuverlassig als Gebuschwald klassifiziert. Die hier vorgestellte Methode unterstreicht das Potenzial von schweizweit verfugbaren Fernerkundungsdaten fur eine weitgehend objektive, kosteneffiziente und grossflachige Kartierung von Gebuschwaldern in einer fur die Praxis genugenden Genauigkeit. Large-scale classification of shrub forest with remote sensing data: Information on shrub forest distribution and development is important for a range of forestry- and ecologically-related questions, but current and area-wide datasets have been characterized by limited availability. In this study, the mapping of shrub forests dominated by green alder, mountain pine and hazel for the canton of Grison was investigated, based on available nationwide remote sensing data. Satellite data from Sentinel-1 and Sentinel-2, as well as a vegetation height and an elevation model were used. Training areas provided by the canton and supplemented by aerial imagery interpretation were used for a supervised classification with Random Forest, a decision tree-based machine learning algorithm. Independent validation of the results was carried out with data from the National Forest Inventory (NFI). Green alder and mountain pine forests were classified with high accuracy of 92.1% respectively 86.7%, whereas for hazel shrub forests, the internal model accuracy was only 66.7%. The resulting area expansion of the shrub forest was comparable with findings based on the NFI. A direct comparison with the NFI aerial imagery interpretation points revealed major discrepancies. The main reason for this is the different degree of spatial detail. However, NFI areas with a high percentage of shrubs were reliably classified as shrub forest. The method presented here underscores the potential of remote sensing data available throughout Switzerland for an essentially objective, costefficient and large-scale mapping of shrub forests with an accuracy applicable in practice.

The potential of Sentinel-2 satellite data for forest applications Sentinel-2 satellite data have been freely available since the end of 2015. The high spatial resolution, the global coverage, the frequent revisit times and the important spectral bands for vegetation analyses open up new perspectives for forest applications. In this study, the potential of Sentinel-2 was investigated through application examples in the forest enterprise of Burgergemeinde Bern on the Swiss Plateau. For this purpose the determination of the deciduous or coniferous proportion, the recording of interventions, as well as the early detection of natural disturbances were tested. Reference data from the forest enterprise (forest inventory, map of harvesting measures, delimitation of a wildfire) were used as training data and for the validation of the results. By using Sentinel-2 data from three different dates, 56% of the variation of the deciduous or coniferous proportion could be explained by a regression model. This model was much better than those that used data from one or two time steps only. The distinction of pure deciduous and coniferous stands achieved an accuracy of 90%. The detection of interventions determined by the differences in the spectral information of Sentinel-2 data from two acquisition dates was highly compliant with the information from the forest enterprise. This was also confirmed by the detection of a forest fire area, which highlights the potential of Sentinel-2 for the early and large-scale detection of natural disturbances.

The combination of terrestrial laser scans and tree-ring data allows for a highly precise reconstruction of annual stem growth, and thus complex tree-growth analyses independent of species and site characteristics. Reliable carbon pools data are needed to quantify the carbon stored in ecosystems and for effective forest management. Terrestrial laser scanning allows researchers to quickly acquire data about forest structure and to derive tree parameters and volume data automatically. However, accurate models of the development of tree volume over time are still lacking. In contrast to terrestrial laser scanning, tree-ring data show the annual growth development of trees, but do not contain information about tree volume. The fusion of terrestrial laser scanning and tree-ring data may, therefore, lead to reliable stem development data, and thus annually resolved models of volume increment of trees. The aim of this study is to combine these data and apply a root-development model to the aboveground part of trees. Three spruce trees (Picea abies) and two firs (Abies alba) which were part of a long-term forest monitoring survey were scanned using a terrestrial 3D-laser-scanner. Combining these data with tree-ring measurements, we were able to reconstruct stem volume at an annual resolution. Results provide robust annually resolved volume data along with ring-width measurements at any point within the modeled tree stem, which present great potential for complex growth analyses. Stem volume, estimated with a bole volume function, deviated between − 1.65 and 1.9% from our model for four out of five trees. For the fifth tree deviations of 13% were observed. The agreement between the function and our model demonstrates the robustness of the presented approach.

International audience; Natural disturbances, such as avalanches, snow breakage, insect outbreaks, windthrow or fires shape mountain forests globally. However, in many regions over the past centuries human activities have strongly influenced forest dynamics, especially following natural disturbances, thus limiting our understanding of natural ecological processes, particularly in densely-settled regions. In this contribution we briefly review the current understanding of changes in forest cover, forest structure, and disturbance regimes in the mountain forests across the European Alps over the past millennia. We also quantify changes in forest cover across the entire Alps based on inventory data over the past century. Finally, using the Swiss Alps as an example, we analyze in-depth changes in forest cover and forest structure and their effect on patterns of fire and wind disturbances, based on digital historic maps from 1880, modern forest cover maps, inventory data on current forest structure, topographical data, and spatially explicit data on disturbances. This multifaceted approach presents a long-term and detailed picture of the dynamics of mountain forest ecosystems in the Alps. During pre-industrial times, natural disturbances were reduced by fire suppression and land-use, which included extraction of large amounts of biomass that decreased total forest cover. More recently, forest cover has increased again across the entire Alps (on average +4% per decade over the past 25-115. years). Live tree volume (+10% per decade) and dead tree volume (mean +59% per decade) have increased over the last 15-40. years. in all regions for which data were available. In the Swiss Alps secondary forests that established after 1880 constitute approximately 43% of the forest cover. Compared to forests established previously, post-1880 forests are situated primarily on steep slopes (gt;30°), have lower biomass, a more aggregated forest structure (primarily stem-exclusion stage), and have been more strongly affected by fires, but less affected by wind disturbance in the 20th century. More broadly, an increase in growing stock and expanding forest areas since the mid-19th century have - along with climatic changes - contributed to an increasing frequency and size of disturbances in the Alps. Although many areas remain intensively managed, the extent, structure, and dynamics of the forests of the Alps reflect natural drivers more strongly today than at any time in the past millennium.

Progress in remote sensing for forestry applications Over the past ten years significant advances have been made in remote sensing data and methods for forestry applications. In many cases datasets are no longer limited to use for case studies or the development of methods, but are available for large area applications, often with high temporal resolution. Aerial image acquisition, including in near infrared, has become standard thanks to the use of digital cameras. Colour infrared orthophotos are easily embedded into GIS applications at the cantonal and national level. Aerial laser scanning data has almost become the norm for many applications. Tools integrated into common working environments are available which allow for the efficient analysis of 3-D point clouds and the realisation of valuable products describing forested areas. Terrestrial laser scanning is also nearing operational applicability for many purposes. Remote sensing is continually developing into a practical instrument for daily work. As long as users are aware of the possibilities and also the limitations, remote sensing offers substantial potential to support and optimise terrestrial inventory and for the generation of additional large-scale values.

Forests dominated by European beech (Fagus sylvatica L.) are among both the most widespread in Europe and the most intensely exploited globally. One of the largest remnants of unmanaged beech forests in Europe is the Uholka-Shyrokyi Luh forest in Transcarpathia, Ukraine, covering 8,800 ha of primeval forest. In 2000, a permanent forest plot of 10 ha has been established in the Uholka massif. All living and dead trees with a diameter at breast height (DBH) ≥ 60 mm were identified to species, DBH measured, stems tagged and mapped. Since then, the plot has been remeasured in 2005, 2010, and 2015. In total, 4,820 individual trees were measured with 14,116 individual measurements throughout all four inventories. In spring 2018, an Airborne Laser Scan was carried out, covering the Uholka-Shyrokyi Luh forest. This data set allows us to derive a high-resolution digital elevation model (DEM) of the plot area. European beech covers a share of ≈ 95% of the basal area (BA) of all living stems. While BA was relatively stable throughout all inventories (≈ 38 m2 /ha), the number of stems increased considerably between 2010 and 2015 from 290 to 430 stems/ha. Additionally, the proportion of beech stems decreased from 95% in 2010 to 86% in 2015. The continuity of the share of beech on BA and the decrease in number of stems can be traced back to disturbance events, which led to an increase of more light demanding species in the recruitment but did not alter the distribution of BA as these small trees contribute very little to BA. The data set allows for important insights into the development and the spatial and temporal dynamics of primeval beech forests. It can be used to quantify the demographic processes growth, mortality, and recruitment, and to study inter- and intraspecific effects on demographic rates, stand structure, and species composition. No copyright or proprietary restrictions are associated with the use of this data set other than citation of this Data Paper.

Mangrove forests in Iran are highly productive and complex ecosystems since they represent the interface between land and sea. They are a unique environment for supporting biodiversity, and they provide direct and indirect benefits to humans. Investigating changes in mangrove forests is essential for ecologists and forest managers to improve the assessment and conservation of natural ecosystems. The goals of the present study include: (I) to evaluate and compare four supervised classification algorithms based on Landsat time series imagery to detect mangrove cover in southern Iran, (II) to detect changes in mangrove cover between 1985, 1998, and 2017; and (III) to compare the four different predictions resulting from the applied classification algorithms. An accuracy assessment was conducted using k-fold cross-validation and independent validation, and differences between the classification techniques were analyzed. Although all four algorithms produced high overall accuracy (ranging from 81% to 93%) and Kappa values (from 0.81 to 0.92), visual comparisons of the predictions revealed that Random Forest (RF) performed best. The results of the change analysis showed that mangrove cover areas decreased by approximately 4% from 1985 to 1998 and then increased by approximately 8.9% from 1998 to 2017. A change detection map shows a decrease in mangrove cover in near coastal regions, such as the Tabl and Gavarzin areas, and an increase in mangrove cover at a distance from the Qeshm coastline that involves open spaces between the trees. Rising water levels and human development are important factors in the decline of mangroves. The findings of this research are useful for the management, restoration and conservation planning of mangrove forest in southern Iran. Keywords: Mangrove, Random forest classifier, Support vector machine, Discriminant analysis, Land cover change, Statistical analysis

Quantiles and proportions in a sampling distribution of a per unit area attribute (Y) depend on the spatial support (area) of employed survey plots. This is a nuisance for managers, and policy developers; in particular when the underlying data have been collected with different spatial supports. Users of these statistics may wish to calibrate their estimates to a common scale of spatial support. The easiest way to do this is through scaling to a common plot size. We demonstrate a statistical method for upscaling. The method is illustrated in the context of a design-based forest inventory of a target attribute Y with a census of a co-located vector of auxiliary variables (X) correlated with Y. Two case studies from Norway and Switzerland confirmed significant and practically important scale effects in quantiles and proportions of above ground live tree biomass (Mg ha−1) and stem volume (m3 ha−1). Upscaling requires an estimate of the spatial autocorrelation of Y given X at the scale of the original spatial support. We present an expedient method to this end. Our method affords estimation of scaled quantiles and proportions and assures consistency of sampling distribution across scales.

Current model of canopy heights in Switzerland: specific applications in forestry A national vegetation height model was calculated for Switzerland for the first time last year using digital aerial images. The ADS80 stereo aerial images, which were routinely gathered by the Swiss Federal Office of Topography (swisstopo), contain information about the height of vegetation in forests. We used the stereo aerial images to first calculate a digital surface model (DSM) with a very high spatial resolution (1 × 1 m). The DSM was then normalized to obtain the actual vegetation heights using a digital terrain model (DTM) based on laser data with the buildings masked out, and to produce a vegetation height model (VHM). Such a model could be calculated in the framework of the Swiss National Forest Inventory (NFI) with consistent methods and a very high level of detail. For covering the whole of Switzerland, we used summer aerial images from the years 2007 to 2012. The VHM reached almost nationwide coverage (98% of the country's surface area). Some areas, such as steep mountainsides or very bright glaciers, were problematic for calculating the model, and appear in it as gaps. Vegetation height information collected with this method is most useful for analyzing entire forest stands, but the data do not have a high enough spatial resolution for single-tree-based analyses. The VHM can be applied in a wide variety of ways. Here, we describe three of these: 1) generating forest masks, 2) extracting forest canopy gaps, and 3) detecting changes in the stocking of a forested area.

Managing forests for ecosystem services and biodiversity requires accurate and spatially explicit forest inventory data. A major objective of forest management inventories is to estimate the standing timber volume for certain forest areas. In order to improve the efficiency of an inventory, field based sample-plots can be statistically combined with remote sensing data. Such models usually incorporate auxiliary variables derived from canopy height models. The inclusion of forest type variables, which quantify broadleaf and conifer volume proportions, has been shown to further improve model performance. Currently, the most common way of quantifying broadleaf and conifer forest types is by calculating the proportions of the corresponding areas of the canopy cover. This practice works well for single-layer forests with only a few species, but we hypothesized that this is not best practice for heterogeneously structured and mixed forests, where the area proportion does not accurately reflect the timber volume proportion. To better represent the broadleaf and conifer volume proportions, we introduced two new auxiliary variables in which the area proportion is weighted by height information from a canopy height model. The main objectives of this study were: (1) to demonstrate the advantage of including forest type (broadleaf/conifer distinction) information in ordinary least squares regression models for timber volume prediction using widely available data sources, and (2) to investigate the hypothesis that including the broadleaf and conifer proportions, weighted by canopy height information, as additional auxiliary variables is favourable over including simple area proportions. The study was conducted in three areas in Switzerland, all of which have heterogeneously structured and mixed forests. Our main findings were that the best model performance can generally be achieved: (1) by deriving conifer and broadleaf proportions from a high-resolution broadleaf/conifer map derived from leaf-off airborne laser scanning data, and (2) by using broadleaf/conifer proportions weighted by height information from a canopy height model. Incorporating the so-derived conifer and broadleaf proportions increased the model accuracy by up to 9 percentage points in root mean square error (RMSE) compared with models not using any forest type information, and by up to 2 percentage points in RMSE compared with models using conifer and broadleaf proportions based solely on the corresponding area proportions, as done in current practice. Our findings are particularly relevant for mixed and heterogeneously structured forests, such as those managed to achieve multiple functions or to adapt effectively to climate change. peerReviewed

Direct assessment of forest regeneration from remote sensing data is a previously little-explored problem. This is due to several factors which complicate object detection of small trees in the understory. Most existing studies are based on airborne laser scanning (ALS) data, which often has insufficient point densities in the understory forest layers. The present study uses plot-based terrestrial laser scanning (TLS) and the survey design was similar to traditional forest inventory practices. Furthermore, a framework of methods was developed to solve the difficulties of detecting understory trees for quantifying regeneration in temperate montane forest. Regeneration is of special importance in our montane study area, since large parts are declared as protection forest against alpine natural hazards. Close to nature forest structures were tackled by separating 3D tree stem detection from overall tree segmentation. In support, techniques from 3D mathematical morphology, Hough transformation and state-of-the-art machine learning were applied. The methodical framework consisted of four major steps. These were the extraction of the tree stems, the estimation of the stem diameters at breast height (DBH), the image segmentation into individual trees and finally, the separation of two groups of regeneration. All methods were fully automated and utilized volumetric 3D image information which was derived from the original point cloud. The total amount of regeneration was split into established regeneration, consisting of trees with a height > 130 cm in combination with a DBH < 12 cm and unestablished regeneration, consisting of trees with a height < 130 cm. Validation was carried out against field-based expert estimates of percentage ground cover, differentiating seven classes that were similar to those used by forest inventory. The mean absolute error (MAE) of our method for established regeneration was 1.11 classes and for unestablished regeneration only 0.27 classes. Considering the metrical distances between the class centres, the MAE amounted 8.08% for established regeneration and 2.23% for unestablished regeneration.

Forest mapping is an important source of information for assessing woodland resources and a key issue for any National Forest Inventory (NFI). In the present study, a detailed wall-to-wall forest cover map was generated for all of Switzerland, which meets the requirement of the Swiss NFI forest definition. The workflow is highly automated and based on digital surface models from image-based point clouds of airborne digital sensor data. It fully takes into account the four key criteria of minimum tree height, crown coverage, width, and land use. The forest cover map was validated using almost 10,000 terrestrial and stereo-interpreted NFI plots, which verified 97% agreement overall. This validation implies different categories such as five production regions, altitude, tree type, and distance to the forest border. Overall accuracy was lower at forest borders but increased with increasing distance from the forest border. Commission errors remained stable at around 10%, but increased to 17.6% at the upper tree line. Omission errors were low at 1%–10%, but also increased with altitude and mainly occurred at the upper tree line (19.7%). The main reasons for this are the lower image quality and the NFI height definition for forest which apparently excludes shrub forest from the mask. The presented forest mapping approach is superior to existing products due to its national coverage, high level of detail, regular updating, and implementation of the land use criteria.

In Switzerland, the decreasing significance of agriculture has led to prominent processes of land abandonment in mountainous areas where the maintenance of open land relies on human intervention. At the same time, urbanisation in Switzerland is increasing at a rapid rate at the expense of other land use types, particularly open land agriculture. In spite of these observed trends, the extent and location of anticipated land-use changes for the coming 20 years remain unknown, as does the impact on landscape services. This research defines 5 scenarios of future land-use for Switzerland along axes of Globalisation to Regionalisation and Market-driven developments to high policy intervention. Using the Dyna-CLUE land use modelling framework we incorporate socio-economic and bio-geographical variables to model scenarios of land change for 2035. By identifying locations for key land use transitions which occur across several scenarios, we find that unless large scale policy interventions are made, large areas of the Swiss Plateau and Alpine valley bottoms face strong urbanisation and much of the mountainous pasture agriculture continues to face risk of abandonment.

Assessing forest cover change is a key issue for any national forest inventory. This was tested in two study areas in Switzerland on the basis of stereo airborne digital sensor (ADS) images and advanced digital surface model (DSM) generation techniques based on image point clouds. In the present study, an adaptive multi-scale approach to detect forest cover change with high spatial and temporal resolution was applied to two study areas in Switzerland. The challenge of this approach is to minimize DSM height uncertainties that may affect the accuracy of the forest cover change results. The approach consisted of two steps. In the first step, a ‘change index' parameter indicated the overall change status at a coarser scale. The tendency towards change was indicated by derivative analysis of the normalized histograms of the difference between the two canopy height models (DCHMs) in different years. In the second step, detection of forest cover change at a refined scale was based on an automatic threshold and a moving window technique. Promising results were obtained and reveal that real forest cover changes can be distinguished from non-changes with a high degree of accuracy in managed mixed forests. Results had a lower accuracy for forests located on steep alpine terrain. A major benefit of the proposed method is that the magnitude of forest cover change of any specific region can be made available within a short time as often required by forest managers or policy-makers, especially after unexpected natural disturbances

Due to the strong impact of the composition and structure of the forest edge on forest ecosystems, the value of investigating changes at the forest edge has been recognized for a few decades. Automatic quantification and detection of forest edge changes has advantages over fieldwork with respect to the time and cost required. Based on the structural features extracted from Airborne Laser Scanning (ALS) data, in this study an automatic method using Kernel Density Estimation (KDE) was developed to describe ALS point data related to the forest edge in terms of a finite number of interpretable indicators. The method was applied to identify patterns in data sets at the forest edge between two given dates (2001 and 2014) in European temperate mixed forests. The study area has an extent of 1403 km2 and the total length of forest boundary is approx. 5000 km. The calculated structural features explained the vegetation’s height distribution and the structural variance at the forest edge. The kernel density curves represented the general structural diversity of the study area. Three extracted indicators described the shape of the kernel density curves and reflected the dynamics of the studied forest edge area. Overall, results from this study show that the proposed approach is a promising tool for using remote sensing data to analyze whether or not the vertical structure of the forest edge for the entire study area has changed and thus to assess automatically the progress of forest management at forest edge.

This study introduces five facets that can improve inference in small area estimation (SAE) problems: (1) model groups, (2) test of area effects, (3) conditional EBLUPs, (4) model selection, and (5) model averaging. Two contrasting case studies with data from the Swiss and Norwegian national forest inventories demonstrate the five facets. The target variable of interest was mean stem volume per hectare on forested land in 108 Swiss forest districts (FD) and in 14 Norwegian municipalities (KOM) in the County of Vestfold. Auxiliary variables from airborne laser scanning (Switzerland) and photogrammetric point clouds (Vestfold) with full coverage and a resolution of 25 m × 25 m (Switzerland) and 16 m × 16 m (Vestfold) were available. Only the data metric mean canopy height was statistically significant. Ten linear fixed-effects models and three mixed linear models were assessed. Area effects were statistically significant in the Swiss case but not in Vestfold case. A model selection based on AIC favored separate linear regression models for each FD and a single common regression model in Vestfold. Model averaging increased, on average, an estimated variance by 15%. Reported estimates of uncertainty were consistently larger than corresponding unconditional EBLUPs.

The ability to map vegetation and in particular individual trees is a key component in forest management and long-term forest monitoring. Here we present a novel approach for mapping individual tree species based on ensemble modeling, i.e. combining the projections of several modeling techniques in order to reduce uncertainty. Using statistical modeling in conjunction with high-resolution aerial imagery (50 cm spatial resolution) and topo-climatic variables (5 m spatial resolution), we map the distributions of six major tree species (3 broadleaf and 3 conifers) in a study area of North-Eastern Switzerland. We also compare the relative predictive power of both topo-climatic and remote-sensing variables for mapping the spatial tree patterns and assess the importance of calibration data quality on model performance. We evaluate our projections using cross-validation as well as with independent data. Overall, the evaluations that we obtain for our vegetation maps are in line with, or higher than, those in similar studies. Depending on the considered tree species, 47.8–85.6% of our samples were correctly predicted, and we obtain an overall CCR (correct classification rate) of 0.72 and a Cohen’s kappa of 0.65. Comparing the predictive power of the different modeling techniques, we find that ensemble modeling (i.e. combining the projections of different individual modeling techniques) generally performs better than individual modeling techniques.

Methods for small area estimations were compared for estimating the proportion of forest and growing stock volume of temperate mixed forests within a district of a member state (canton) in Switzerland. The estimators combine terrestrial data with remotely sensed auxiliary data. By using different model types, different sources of auxiliary data and different methods of processing the auxiliary data, the increase in estimation precision was tested. Using the canopy height derived from remote sensing data, the growing stock volume and the proportion of forest were estimated. The regression models used for the small area estimation provided a coefficient of determination of up to 68% for the timber volume. The proportion of plots correctly classified into forest and non-forest plots ranged between 0.9 and 0.98. Models calibrated over forest area only resulted in a maximal coefficient of determination of 37%. Even though these coefficients indicate a moderate model quality, the use of remote sensing ...

Trees outside forest (TOF) can perform a variety of social, economic and ecological functions including carbon sequestration. However, detailed quantification of tree biomass is usually limited to forest areas. Taking advantage of structural information available from stereo aerial imagery and airborne laser scanning (ALS), this research models tree biomass using national forest inventory data and linear least-square regression and applies the model both inside and outside of forest to create a nationwide model for tree biomass (above ground and below ground). Validation of the tree biomass model against TOF data within settlement areas shows relatively low model performance (R 2 of 0.44) but still a considerable improvement on current biomass estimates used for greenhouse gas inventory and carbon accounting. We demonstrate an efficient and easily implementable approach to modelling tree biomass across a large heterogeneous nationwide area. The model offers significant opportunity for improved estimates on land use combination categories (CC) where tree biomass has either not been included or only roughly estimated until now. The ALS biomass model also offers the advantage of providing greater spatial resolution and greater within CC spatial variability compared to the current nationwide estimates. Electronic supplementary material The online version of this article (doi:10.1007/s10661-017-5816-7) contains supplementary material, which is available to authorized users.

Combining data from the Swiss National Forest Inventory and from remote sensing for small-area estimations in forestry A design-unbiased small estimator was tested in this study. This estimator combines terrestrial data from the Swiss National Forest Inventory (LFI) with ancillary data from stereo aerial images and airborne laser scanner (ALS) data. The estimator was tested for the two target variables: the percentage of forest and the timber volume. The efficiency of the estimator depends on the model precision of the target variable obtained with remote sensing data and other ancillary spatial data, which can potentially explain the spatial variation of the target variable. Canopy heights derived from stereo aerial images (ADS40) and ALS data were used as ancillary data. Regression models for the timber volume and the forest/non-forest decision of the LFI samples were calibrated within the cantons Appenzell Inner Rhodes and Appenzell Outer Rhodes and provided a coefficient of determination of roughly 60%. Adding the forest/non-forest decision from the aerial photo interpretation of the LFI as an explanatory variable slightly improved the models and allowed to gain a coefficient of determination of 65% for the timber volume and 85% for the forest/non-forest decision. Within the forest area, the canopy height models explained nearly 40% (ALS data) and 20% (ADS40 data) of the observed timber volume variability. This case study shows that using remote sensing data can increase the precision (in terms of the standard error) of the timber volume estimation in canton Appenzell Inner Rhodes by roughly 30%. The same is valid for the estimation of the percentage of forest. A reduction in the standard error of about 10% for the latter estimation was obtained by using the aerial images and nearly 25% using the ALS data.

Development of forest area in Switzerland within the last 120 years Historical maps provide a useful source for spatial landscape analyses as they represent a generalized reflection of geographical reality over large areas at a certain time. Digitization and georeferencing of analog maps help to make such analyses more efficient. In Switzerland, the forest area has grown since the mid 19th century according to statistics of the federal government and the cantons. These statistics, however, provide a limited basis for regional analyses of changes in forest area. In this study, the forest areas in 1880, 1915, 1940 and 2000 were estimated based on analyses of the Topographical Atlas of Switzerland (Siegfried maps [1880Siegfried maps [1915, and 1940]), and of the Topographical Map (2000). Using a sample of 20 638 plots, analogous to the aerial photo-interpretation of the Swiss National Forest Inventory (NFI), a forest/non-forest decision was made for each time cutting. Overall, in Switzerland the forest area increased by 21.6% from 1880 to 2000. This is much less than what revealed the analysis of statistical sources due to differences in the methods and forest definitions used. Considerable regional differences could be detected, with one general trend from north to south and another from east to west. In most regions with strong urban development, the forest area is stagnating or has slightly decreased. The increase in forest cover has taken place mainly in the Alps and the Southern Alps.

Tree cover in Switzerland The requirements for national forest inventories have changed in recent decades, as have the issues involved. Initially, the focus was mainly information on timber resources, but today social and environmental functions are also of interest. An a priori separation of the surveyed areas into forest and non-forest during data collection limits the interpretation of the tree resources. Not all trees are located in the forest and not all forests are fully stocked. In the aerial photo interpretation of the 3rd National Forest Inventory, land cover on a regular sampling grid was determined regardless of the land use. This allowed, for the first time, nationwide information on tree resources to be obtained, independent of forest definitions. The tree cover of Switzerland is 27.0% regardless of whether the trees are in the forest or outside. The area covered with forest (proportionally 29.4%) is larger than that covered with trees. The location of the trees outside the forest tends to be mostly either very close to forest or in urban areas. The most densely stocked areas, after forests, are urban areas. The data from aerial photo interpretations of the 3rd National Forest Inventory allow a more nuanced picture of the stocking over the whole country, but the sampling error is still too large to draw conclusions for small areas. The existing and ongoing surveys, however, provide a calibration and reference dataset so that, with remote sensing data and methods, it should be possible to generate more comprehensive spatial datasets to help to fill this gap.

The limestone cliffs of the Jura mountains in northwestern Switzerland harbour a diverse flora with several relic populations of arctic-alpine and Mediterranean plant species. Overgrowing forests increasingly threaten rare, relic plant species with high light demands on cliffs, because traditional forms of forest use, including the collection of firewood and grazing by goats, were abandoned. We examined the temporal and spatial patterns of overgrowing forest at six cliffs in the northwestern Swiss Jura mountains by analysing tree cover on time-series air photographs made between 1951 and 2000. Overall tree cover increased from 60% to 85% at the six cliffs examined between 1951 and 1964 and then levelled off. The increase in tree cover showed distinct spatial patterns. The cover increased significantly in the talus and on the cliff face, but not on the plateau (at the top of the cliffs). Our results confirm the assumption that the cliff forests became denser during the last decades. Forestry practices such as selective thinning or controlled grazing by goats are suggested as a method of promoting the threatened relic plant species.

Forest mapping at the landscape scale for resource planning and monitoring usually applies a discrete forest definition based on the primary forest use. Forest uses, however, can change in space and time. Therefore, the dependent forest definition may no longer be appropriate. We analysed how the Swiss National Forest Inventory partitions the continuous landscape resources into discrete forest/non-forest classes of the Swiss Jura mountain range and how such a binary classification affects the representation of forest resources in space. Additionally, we performed a sensitivity analysis to assess the effect of different forest classification thresholds representing different forest definitions on the spatial estimates of the dependent forest area. Relevant landscape variables for forest area estimations were sampled from coloured aerial imagery in the Swiss Jura mountain range on a rectangular 500 m × 500 m spaced grid. Each sample plot of 50 m × 50 m dimension containing all measured landscape variables was then classified as forest or non-forest based on the Swiss National Forest Inventory definition, as well as other selected thresholds along the landscape variables’ range. The resulting forest area as defined by the Swiss National Forest Inventory covered 45.2% of the Jura mountain range containing 86.7% of the overall tree vegetation. In agreement with the original forest use, tree vegetation on forest sample plots was taller, denser and stands were larger than on non-forest plots. However, we identified considerable amounts of tree vegetation located outside of the forest dispersed all over the landscape. 73.3% of all the sample plots contained some fractions of tree vegetation, while only 18.0% were fully covered by trees. Among the different continuous landscape variables tree canopy cover had a large effect on forest area estimates, while the effects of tree canopy height and stand width were found to be moderate. This study confirmed that the applied forest definition of the Swiss National Forest Inventory extracted the spatial domain of forests appropriately if the original forest use is of interest. However, our results also indicate that a change in forest use and hence forest definition result in considerably different spatial pattern and forest area estimation. In the presented approach forest becomes a dependent variable, whereas the independent raw data is represented by continuous landscape variables. As a consequence, different forest definitions for different forest uses can be applied in a subsequent analysis step. By this, forest area estimates can be adapted easily and consistently to a range of new forest uses as applied in the third Swiss National Forest Inventory.

The forest fire area above Leuk (Canton Valais, Switzerland)has served since 2004 for several studies on ecological resilience. On the 300 ha patch that ranges from 800 to 2100 m a.s.l., several samples have been installed. Vegetation changes were monitored representatively on a grid of 125 m cell size. The vegetation of the intact forest vegetation could be compared on a formerly assessed stratified sample and by re-assessing the actual vegetation. Along six horizontal transects at different altitudes, soil samples were taken from different layers of 25 soil profiles. Invertebrate fauna was collected weekly during the summer season at 18 localities along three transects at different altitudes. Localities were grouped into the three types«intact forest», «burnt forest» and «transition zone». One year after the fire, following results are at hand: For a wide area,the vegetation was literally missing. Surprisingly, at scales of 0.25 ha, the actual number of plant species was not significantly different from the former intact forest vegetation. As an indicator of fire intensity, the ash layer was significantly and negatively correlated with the vegetation cover in 2004. At altitudes between 1200 and 1600 m a.s.l., the fire intensity turned out to have been most severe. The fire resulted in a general increase of the pH in the top soil by means of 1 to 1.5 units if compared to unburned top soils of the corresponding communities,basically as an effect of burnt organic acids and by the loss of protons. A superficial hydrophobic carbonate layer resulted from cations freed by the burning process. In consequence,erosion processes on steep places were intensified. Invertebrate diversity was surprisingly high one year after the forest fire. The most frequent groups were beetles, aculeateHymenoptera, two-winged flies, and parasitic wasps. About 50% of the collected individuals were pollinators and 28,5%were predators. Highest individual numbers were counted in the transition zone. The started projects will be continued in the next few years.