Dr. Berthold Traub

Forests are under pressure and going through rapid changes. However, current inventorying and monitoring (IM) programs are often either disjointed, too narrow in their scope and/or do not operate at fine enough temporal resolutions, which may hinder scientific understanding, the timely supply of information, fast decision making, and may result in the sub-optimal use of resources. For these reasons, there is an urgent need for Advanced Forest Inventorying and Monitoring (AIM) programs to (i) achieve expanded relevance (by augmenting data/information across ecosystem properties and trophic levels), (ii) have increased temporal resolution (by tailored data collection frequency), and (iii) make use of technological advances (by incorporating novel tools and technologies). The Advanced Inventorying and Monitoring for Swiss Forests (SwissAIM) initiative was launched in 2020 to address these needs. SwissAIM builds upon the foundation offered by the existing programs (e.g., national forest inventory, long-term forest ecosystem research, biodiversity monitoring). It aims to offer a collaborative and adaptive framework to enable integrated data collection, evaluation, interpretation, analysis, and modeling. Ideally, it will result in a more responsive system with respect to current and predicted biotic/abiotic stressors that will challenge Swiss forests. Developing such a system implies identifying the information needs of different stakeholders (e.g., science, policy, practice), related technical requirements, and governance frameworks. Here, we present (i) the main features of the SwissAIM initiative (vision, scientific questions and variables, governance and engagement), (ii) the main outcomes of the participatory design process (measurements, sampling, and plot design), (iii) the potential transferability of AIM initiatives outside Switzerland (timing, relevance, practicability), and (iv) the key messages that emerged (i.e., need for advancement, integration and transdisciplinarity, statistical underpinning). Since similar needs related to forest inventorying and monitoring are emerging throughout Europe and elsewhere, the objective of this opinion paper is to share our experience and promote a dialog with those interested in developing AIM initiatives in other countries and regions.

Seit bald vier Jahrzehnten werden im Rahmen des Landesforstinventars (LFI) der Zustand und die Entwicklung des Schweizer Waldes beobachtet. Bislang sind vier Stichprobeninventuren durchgeführt worden. Derzeit werden die Daten für die fünfte Inventur erhoben. Seit 1996 führt das Amt für Wald und Naturgefahren (AWN) des Kantons Graubünden eine kantonale Waldinventur in Zusammenarbeit mit der Eidgenössischen Forschungsanstalt für Wald, Schnee und Landschaft (WSL) durch. In diesem Artikel wird die «LFI-Methode» kurz vorgestellt und einige Aspekte der langjährigen Zusammenarbeit zwischen der WSL und dem AWN hervorgehoben.

Habitatbäume sind eine Schlüsselkomponente der Waldbiodiversität. Seit rund 20 Jahren ist die Bedeutung von Baummikrohabitaten (BMH) als Lebensraum für Tausende teils hochspezialisierte Arten bekannt, und BMH finden zunehmend Eingang ins Waldmonitoring. Mit der Einführung der ersten europäischen Typologie der BMH verfügen das Schweizerische Landesforstinventar (LFI) und die Inventur im Buchenurwald Uholka-Schyrokyj Luh in der Ukraine als erste Grossrauminventuren über repräsentative Referenzdaten. Auch wenn die Reproduzierbarkeit bei einigen BMH-Typen eher schlecht ist, sind nach drei Erhebungsjahren des fünften LFI doch erste aufschlussreiche Vergleiche möglich. Im Schweizer Wald weisen 60% der stehenden lebenden Bäume ab 12 cm Brusthöhendurchmesser mindestens ein BMH auf. Am häufigsten in Stück pro Hektare sind solche mit Flechtenbewuchs (73), Moosbewuchs (67), Stammfusshöhlen (53), Kronentotholz (47) und Harzfluss (37), selten jene mit tiefen Mulmhöhlen (4), Spalten (1), Spechtbruthöhlen (1) und Pilzfruchtkörpern (1). Am geringsten ist die Anzahl BMH-Typen an Tannen (0.6), am grössten an Arven (1.6), Eichen (1.4) und Kastanien (1.3). Generell nimmt die Anzahl verschiedener BMH-Typen mit zunehmendem Baumdurchmesser zu. Die BMH-Dichte variiert je nach Waldfunktion, Waldeigentum, letzter Nutzung, Höhenlage und Geländeneigung. Die tiefen Werte im Mittelland und im östlichen Jura könnten Handlungsbedarf indizieren. Die BMH-Dichte an lebenden Bäumen ist im Schweizer Buchenwald gleich gross wie im Buchenurwald Uholka-Schyrokyj Luh. Allerdings sind tiefe Baumhöhlen, Rindentaschen, Kronentotholz und mehrjährige Pilzfruchtkörper im Buchenurwald häufiger, im Buchenwald dagegen Dendrotelme, Moos- und Efeubewuchs. Der wohl wichtigste ökologische Unterschied liegt im Totholzvolumen, das im Buchenurwald insgesamt fast dreimal so hoch ist wie im Buchenwald, bei den Dürrständern sogar fast sechsmal so hoch.

Habitat trees are a key component for forest biodiversity. Tree-related microhabitats (TreM) were identified about 20 years ago as important habitats for thousands of species, some of which are highly specialised, and are increasingly being taken into consideration in forest monitoring. With the introduction of the first European TreM typology, the Swiss National Forest Inventory (NFI) and the inventory in the primeval beech forest Uholka-Schyrokyj Luh in Ukraine became the first large-scale inventories to have representative reference data. Although the reproducibility of some TreM types is rather poor, it is now possible, after three survey years of the fifth NFI, to make initial informative comparisons. In the Swiss forest, 60% of the standing living trees with a diameter at breast height of 12 cm or more have at least one TreM. The most frequent TreMs per hectare are those with lichen cover (73), moss cover (67), buttress-root concavities (53), crown deadwood (47), and resin flow (37), whereas those with deep rot holes (4), cracks (1), woodpecker breeding cavities (1), and fungal fruiting bodies (1) are rarer. The tree with the fewest TreM types is the silver fir (0.6), and the most are found on stone pines (1.6), oaks (1.4) and chestnuts (1.3). In general, the number of different TreM types increases with increasing tree diameter. TreM density varies according to forest function, forest ownership, previous use, altitude, and slope. The low frequency on the Central Plateau and in the eastern Jura could indicate that action is needed. The TreM density on living trees in Swiss beech forests is the same as in the Uholka-Schyrokyj Luh primeval beech forest. However, deep tree cavities, bark pockets, crown deadwood and perennial fungal fruiting bodies are more common in Ukraine’s primeval beech forest. In contrast, dendrotelms, moss and ivy growth are more common in Switzerland’s beech forests. Probably the most important ecological difference is in the volume of deadwood, which is almost three times higher in the primeval beech forest than in Swiss beech forests, and as much as six times higher if the focus is on standing dead trees.

Das Landesforstinventar (LFI) ist eine periodische Erhebung des Bundes über den Zustand und die Entwicklung des Schweizer Waldes. Im vorliegenden Kapitel werden die Geschichte und die Ziele sowie die Methoden der Datenerhebung und -analyse des LFI vorgestellt, soweit sie für das Verständnis und die Interpretation der im Bericht dargestellten Ergebnisse notwendig sind.

L'inventaire forestier national (IFN) est un relevé périodique de l'état et du développement de la forêt suisse conduit par la Confédération. Ce chapitre présente l'historique et les objectifs de l'inventaire ainsi que les méthodes de collecte et d'analyse des données pour faciliter la compréhension des résultats contenus dans ce rapport.

Background: Under ongoing climate and land-use change, biodiversity is continuously decreasing and monitoring biodiversity is becoming increasingly important. National Forest Inventory (NFI) programmes provide valuable time-series data on biodiversity and thus contribute to assessments of the state and trends in biodiversity, as well as ecosystem functioning. Data quality in this context is of paramount relevance, particularly for ensuring a meaningful interpretation of changes. The Swiss NFI revisits about 8%-10% of its sample plots regularly in repeat surveys to supervise the quality of fieldwork.
Methods: We analysed the relevance of observer bias with equivalence tests, examined data quality objectives defined by the Swiss NFI instructors, and calculated the pseudo-turnover (PT) of species composition, that is, the percentage of species not observed by both teams. Three attributes of woody species richness from the latest Swiss NFI cycles (3 and 4) were analysed: occurrence of small tree and shrub species (1) on the sample plot and (2) at the forest edge, and (3) main shrub and trees species in the upper storey.
Results: We found equivalent results between regular and repeat surveys for all attributes. Data quality, however, was significantly below expectations in all cases, that is, as much as 20%-30% below the expected data quality limit of 70%-80% (proportion of observations that should not deviate from a predefined threshold). PT values were about 10%-20%, and the PT of two out of three attributes decreased significantly in NFI4. This type of uncertainty - typically caused by a mixture of overlooking and misidentifying species - should be considered carefully when interpreting change figures on species richness estimates from NFI data.
Conclusions: Our results provide important information on the data quality achieved in Swiss NFIs in terms of the reproducibility of the collected data. The three applied approaches proved to be effective for evaluating the quality of plot-level species richness and composition data in forest inventories and other biodiversity monitoring programmes. As such, they could also be recommended for assessing the quality of biodiversity indices derived from monitoring data.

In this chapter, we discuss several types of quality assurance (QA) and quality control (QC) measures, which are described for the three main stages in the field survey in sequence. This comprises the process of aerial-image interpretation, which is crucial for ensuring the complete coverage of the Swiss forest in the field sample. We also discuss the QA activities applied during fieldwork, including the training of field teams, the maintenance of the field manual, and several organisational measures to prevent mistakes. Finally we describe the additional surveys conducted to assess the quality (reproducibility) of the field assessments in terms of precision (uncertainty) and accuracy, and summarise the quantitative results of selected attributes from the Swiss National Forest Inventory (NFI)'s repeat survey.

The NAtional Forest Inventory Data Analysis System (NAFIDAS) is a multitier application to store and analyse Swiss National Forest Inventory (NFI) data. It provides a basis to publish findings about the current state and development of the Swiss forest on a regional and national level. The system supports the complete chain of data processing of a sample-based forest inventory, starting with the upload of field data and ending with the publication of tables and maps on the Internet. It also provides the necessary tools for administering and maintaining the system and offers access to lists of variables, derivations and dependencies, as well as to documentation of code and production processes. NAFIDAS creates concise result tables and maps with reproducible statistics on NFI data including standard errors and allows the publication of multilingual results based on the most recent NFI data. The efficient long-term storage facilities of raw and derived inventory data form   the backbone of the system.
B.
 

Swiss National Forest Inventory (NFI) produces estimates of characteristics related to the state and change of the forest resources in the whole country as well as in smaller regions and domains. The estimates are based on measurements on permanent field plots placed on a systematic grid over the country. Digital stereo aerial images are an important source of auxiliary information that can be used with post-stratified estimation to increase the precision of the estimates. We examined post-stratification based on stereo image information, available on a much denser grid than the field plots, for the estimation of forest area, total growing-stock volume and growing-stock volume per unit of accessible forest area. We considered 31 post-stratification schemes that consisted of (1) forest/non-forest stratification by four alternative methods based on manual or semi-automated stereo image interpretation, and (2) sub-stratification within the forest stratum by regression-tree models constructed between field-measured growing-stock volume and stereo-image-retrieved vegetation height characteristics. We applied the schemes in two real NFI estimation cycles, and found that the most precise schemes reduced the variance of the whole-country estimators of forest area, total growing-stock volume and growing-stock volume per unit of accessible forest area by 81%, 67% and 34%, respectively, compared to the estimator without post-stratification. Based on this study, we consider regression-tree modelling well-suited for post-stratification, as it produces stratum boundaries as a direct result of modelling, appears robust against deviating values in fitting data, and avoids problems with unrealistic model predictions in application.

Many countries conduct national forest inventories and collect data from different sources such as field sample plots or aerial imagery. The sustainable management of this data requires appropriate systems for the storage and dissemination of this data to a variety of stakeholders. The Swiss national forest inventory started with surveys in 1982 and developed the NAFIDAS system which facilitates highly metadata controlled management and analysis of sample based forest inventory data. Public users are provided with guided access to a comprehensive set of tables and interactive maps on forest statistics. A very user-friendly search and filtering system supports the querying of results. NAFIDAS has proven to be a performant solution which may serve as a blueprint for the complete data processing chain of sample based forest inventories starting with the upload of data and ending with the provision of a huge variety of inventory tables and maps on the internet.

The Swiss National Forest Inventory (NFI) repeats surveys to guarantee the quality of fieldwork. To this end, approximately 10% of sample plots are completely surveyed a second time over a field season. Based on the results of the repeat survey, the current investigation focuses on the assessment precision, i.e. the reproducibility of various tree and stand attributes in NFI4. It also investigates whether the change from periodic (NFI1–NFI3) to continuous (NFI4) fieldwork has had a positive effect on the reproducibility of the attributes. The current results of the repeat surveys for NFI4 (2009/2017) are compared with those for NFI3 (2004/2006) to this end. We used statistical measures as well as measurement quality objectives (MQO) set by the NFI instructor team as a reference for evaluating reproducibility. The results vary for tree attributes which are vital for estimating stock. The result for the diameter at breast height (dbh) corresponds to the expected values, while that for upper stem diameter at seven meters height and tree height were approximately 5% below the expected values. With regard to the seven stand attributes also analyzed, four of them exceeded the quality goals (stand age, stand stability, the degree of cover of secured regeneration, and stage of development). The results for the mixture proportion, the stand structure and crown closure were between 5 and 18% below MQO. The result for presence of woody species shows that the recording of larger plants (above 130 cm) is clearly more reproducible than for smaller plants (40–130 cm). In NFI4, the reproducibility for almost all studied attributes was improved. The results suggest that the modified structure of fieldwork (with only three field teams and continuous fieldwork in NFI4) has a positive influence on the reproducibility of the included attributes.

The variable documentation provides information about the attributes that were used in the second National Forest Inventory. The documentation is intended to give a rough overview. A detailed description of the individual attribute values and code definitions is therefore not given at this point. With abbreviated names and the name of the table, it ensures that the attributes are clear and the reference to the database of the second NFI is given. The data for the individual attributes are stored in a relational database in thematic tables and can be linked with the help of key variables (which are not listed here) for the analysis. The list at hand is sorted by themes (=tables) and abbreviated names.

The inventory concept of the National Forest Inventory requires the analysis software to be highly flexible. The current value of various measures of the Swiss forests and their changes since the first inventory are of interest. A breakdown of the results into geographical units, such as production regions, cantons, or economic regions is just as important as the formation of assessment units, which allows further differentiation of the results according to categories, such as ownership or forest type. [...]

The Swiss National Forest Inventory established, like most of the European national forest inventories, permanent sample plots, which were repeatedly visited and measured. In contrast to these were temporary sample plots, which were only measured during one particular inventory. In a few of the cases, such as the French Forest Inventory, only temporary sample plots were used. Frequently, permanent as well as temporary samples were used during one inventory.
The terrestrial sample grid of the second Swiss National Forest Inventory was a subsample of the 1 km x 1 km grid (in the following referred to as 1-km-grid) of the first NFI. The samples were systematically located at the diagonal corner points of the first NFI grid, so that the resulting grid had a width of 1.44 km x 1.44 km (in the following referred to as 1.4-km-grid). (See Chapter 2.1.5, Table 4, BRÄNDLI and BRASSEL 1999.) The reduced sample size, and changes from forest to non-forest, created different categories of sample plots in the second NFI which were similar to the "sampling with partial replacement" (KÖHL 1994; WARE and CUNIA 1962). [...]

Multi-national statistics are frequently based on data, which originate from national surveys. The systems of nomenclature applied for key attributes often show national differences. Different error sources which are incorporated in multi-national statistics are discussed. The paper presents approaches for harmonisation and standardisation of multi-national environmental statistics and gives examples from the forestry sector. The effect of differences of national forest area estimates on multi-national figures is quantified. An example from forest health surveys is presented that shows the impact of different interpretation and application of the attribute "crown transparency" that is already harmonised on the European level.

Forest inventory data on the national scale cover a broad range of information. Attributes on forest production still play an important role, however ecologically relevant data on forest structure and species diversity get increasing attention. Typically, forest inventories are sample based and the estimation of ecological relevant information from inventory data is restricted. Indicators as e.g. patch perimeter are usually measured from maps, if those indicators cannot be measured directly - e.g. due to the absence of maps- it might be of interest how they could be estimated from sample based inventory data. Methods to estimate the forest boundary length are presented and applied to data of the second Swiss National Forest Inventory (NFI). The forest area definition of the Swiss NFI is based on fine grain data derived from air photo plots classified to nonforest and forest categories. Both, original photo plot interpretation data as well as the relationship between the classification results; (1) interior forest plots, (2) nonforest plots and (3) forest plots intersecting a forest border are employed. The presented methods allow to estimate forest boundary length figures from successive inventory occasions and thus add a landscape ecological component to already established sample based inventory systems.

Seit 1995 ist die WSL im Rahmen des Kirgisisch-Schweizerischen Forstprogramms (KIRFOR) an der Entwicklung des forstlichen Sektors in Kirgisien beteiligt. Mehrere Mitarbeiter der WSL waren im Auftrag der Schweizerischen Gesellschaft für Entwicklung und Zusammenarbeit, Intercooperation (IC) am Aufbau eines Forstinventursystems beteiligt.
Das 1999 in Kraft getretene neue Forstgesetz Kirgisiens fordert Erhalt der Nachhaltigkeit. Biodiversität und Holzproduktion haben einen gleichwertigen Status; Pflanzungen und Naturverjüngung sollen die Waldfläche vergrössern. Die forstwirtschaftlich notwendigen Entscheidungen sollen unter Einbezug der lokalen Bevölkerung möglichst auf Betriebsebene getroffen werden. In die Bewirtschaftung sollen auch private Unternehmer eingebunden werden.
Ein Ziel des KIRFOR-Programms ist es, die existierenden Forsteinrichtungseinheiten
bei der Entwicklung der nationalen Forstinventur zu unterstützen. Hohe Priorität hat der Transfer der lokal vorhandenen Erfahrung in die zentrale Forstverwaltung einerseits und die Vermittlung der zentralen Forstpolitik in die Betriebe andererseits. Die Umsetzung erfolgt in zentralen und lokalen Projekten. Das neue Inventursystem besteht aus einer kombinierten Forstinventur auf nationaler und betrieblicher Ebene. Die methodischen Entwicklungen sind grösstenteils abgeschlossen; die Betriebsinventur hat in verschiedenen Testbetrieben bereits begonnen. Derzeit unterstützt die WSL eine Gruppe von GIS-Spezialisten bei der Entwicklung einer Datenbank und von Software zur Erfassung und Auswertung der Inventurdaten.
Nachfolgend wird die Entwicklung der Forstinventur in Kirgisien kurz skizziert und die Rolle der WSL im KIRFOR-Programm der IC erläutert. Ausführliche Beschreibungen zum Thema Forstwirtschaft und Forstinventur in Kirgisien finden sich in SCHEUBER et al. (2000a) und SCHEUBER et al. (2000b).