The data storage and analysis system of the Swiss National Forest Inventory

Traub, Berthold; Meile, Rolf; Speich, Simon; Rsler, Erik

doi:10.1016/j.compag.2016.11.016

Cited by 23 publications

(15 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Tree variables were obtained directly from the NFI database (Traub, Meile, Speich, & Rösler, 2017). At the individual tree level, we considered variables DBH and basal area of trees larger than the target tree (BAL, m 2 /ha), which was used as a proxy for competition within the forest plot.…”

Section: Factors Influencing Tree Growthmentioning

confidence: 99%

Multiple factors modulate tree growth complementarity in Central European mixed forests

et al. 2017

View full text Add to dashboard Cite

Mixed species forests can often be more productive and deliver higher levels of ecosystem services and functions than monocultures. However, complementarity effects for any given tree species are difficult to generalize because they can vary greatly along gradients of climatic conditions and resource availability. Identifying the conditions where species diversity can positively influence productivity is crucial. To date, few studies have examined how growth complementarity across species and mixture types is modulated by stand and environmental factors, and fewer have considered more than one or two factors. We investigated how complementarity effects for several major Central European tree species change with climatic and edaphic conditions, and with stand structural characteristics, including species composition. We used data from the Swiss National Forest Inventory, which is based on 3,231 plots of pure and mixed stands (19 mixture types) across a broad environmental gradient, to test (i) how mixing effects change depending on the identity of the admixed species and (ii) if complementarity consistently increases when environmental conditions become harsher. The magnitude, whether positive or negative, of complementarity increased with increasing stand density and stand developmental stage, but no general pattern could be identified across mixture types. Complementarity for many species increased as drought intensity and temperature increased, but not for all species and mixture types. While soil conditions, nitrogen and site topography influenced complementarity for many species, there was no general pattern (increases and decreases were observed). Synthesis. Our study indicates that complementarity varies strongly with stand density and stand development as well as with topographic, climatic and soil conditions. This emphasizes the need to account for site‐dependent conditions when exploring mixture effects in relation to forest productivity. We found that under certain conditions (i.e. increasing drought, higher temperature), mixed forests can promote individual tree growth in Central European temperate forests. However, careful assessments depending on the species composing the stands are required under changing resource availability as well as under different levels of stand density and development.

show abstract

Section: Factors Influencing Tree Growthmentioning

confidence: 99%

Multiple factors modulate tree growth complementarity in Central European mixed forests

et al. 2017

View full text Add to dashboard Cite

show abstract

“…For this reason, forest inventory programs have largely been harmonized [5][6][7]. On the one hand, indicators of forest health used in forest inventory programs are accurate, but on the other hand, their acquisition is time and cost-intensive, so that in most cases plot-based monitoring has to be carried out at long intervals (>1 year).Today, an increasing amount of forest health data is available from national forest inventories and monitoring programs [8,9], experimental studies of forest ecosystems [10], as well as from remote sensing (RS) data and RS data products [11][12][13][14]. In addition, RS technologies can be used to derive a large range of forest health indicators [6,[15][16][17].…”

mentioning

confidence: 99%

“…Today, an increasing amount of forest health data is available from national forest inventories and monitoring programs [8,9], experimental studies of forest ecosystems [10], as well as from remote sensing (RS) data and RS data products [11][12][13][14]. In addition, RS technologies can be used to derive a large range of forest health indicators [6,[15][16][17].…”

mentioning

confidence: 99%

Understanding Forest Health with Remote Sensing, Part III: Requirements for a Scalable Multi-Source Forest Health Monitoring Network Based on Data Science Approaches

et al. 2018

View full text Add to dashboard Cite

Forest ecosystems fulfill a whole host of ecosystem functions that are essential for life on our planet. However, an unprecedented level of anthropogenic influences is reducing the resilience and stability of our forest ecosystems as well as their ecosystem functions. The relationships between drivers, stress, and ecosystem functions in forest ecosystems are complex, multi-faceted, and often non-linear, and yet forest managers, decision makers, and politicians need to be able to make rapid decisions that are data-driven and based on short and long-term monitoring information, complex modeling, and analysis approaches. A huge number of long-standing and standardized forest health inventory approaches already exist, and are increasingly integrating remote-sensing based monitoring approaches. Unfortunately, these approaches in monitoring, data storage, analysis, prognosis, and assessment still do not satisfy the future requirements of information and digital knowledge processing of the 21st century. Therefore, this paper discusses and presents in detail five sets of requirements, including their relevance, necessity, and the possible solutions that would be necessary for establishing a feasible multi-source forest health monitoring network for the 21st century. Namely, these requirements are: (1) understanding the effects of multiple stressors on forest health; (2) using remote sensing (RS) approaches to monitor forest health; (3) coupling different monitoring approaches; (4) using data science as a bridge between complex and multidimensional big forest health (FH) data; and (5) a future multi-source forest health monitoring network. It became apparent that no existing monitoring approach, technique, model, or platform is sufficient on its own to monitor, model, forecast, or assess forest health and its resilience. In order to advance the development of a multi-source forest health monitoring network, we argue that in order to gain a better understanding of forest health in our complex world, it would be conducive to implement the concepts of data science with the components: (i) digitalization; (ii) standardization with metadata management after the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles; (iii) Semantic Web; (iv) proof, trust, and uncertainties; (v) tools for data science analysis; and (vi) easy tools for scientists, data managers, and stakeholders for decision-making support.forest ecosystem change through effective global coordination [3] good observations, indicators and scenarios of biodiversity and ecosystem services change [4].There is still a large discrepancy between the information required by forest managers and scientists and the information that is available for understanding and assessing the complexity and multidimensionality of forest health drivers, stressors, disturbances, and effects. Decision makers require information on forest health in high spatial and temporal accuracy, from the local to the global, for short and long-term periods that can be recorded ...

show abstract

“…Our presentation of MI results and discussion has centered on MI as a tool in support of inference. It is equally clear that one might consider imputed data as a replacement for missing values in a NFI data base (Eskelson et al 2009b, Traub et al 2017. However, this may generate bias in estimates of change over time when, with the privilege of hindsight, an imputation turns out to be an outlier.…”

Section: Discussionmentioning

confidence: 99%

Updating Canada’s National Forest Inventory with multiple imputations of missing contemporary data

Magnussen

StinsonGraham

BoudewynPaul

2017

The Forestry Chronicle

View full text Add to dashboard Cite

Canada's National Forest Inventory (NFI) is facing an issue of spatial imbalance in photo interpreted data from 400 ha photo-plots available for estimation of state and change. Multiple imputations (MI) of missing data is therefore considered as a means to mitigate a potential bias arising from spatial imbalance, and-to a lesser degree-improve the precision relative to what can be achieved with the subset of plots having current data. In this study we explored MI with data from three study sites located in the provinces of Quebec, Ontario, and Saskatchewan. Specifically, we looked at state at time T 2 and change between T 1 and T 2 in cover-type area proportions and in per unit area stem volume. At each location we found significant T 1 differences in these attributes between plots with and without T 2 data. A MI procedure with 20 replications of stochastic model-based imputations of missing data was therefore effective as a way to mitigate a bias that would arise if T 2 inference was based exclusively on plots with T 2 data. Possible differences between the T 2 and T 1 photointerpretation, paired with no efficient stratification of disturbed and undisturbed plots, largely eliminated expected gains in precision from the MI boosting of the effective T 2 sample size. Despite recognized limitations, we recommend MI as an effective tool to counteract an emerging spatial imbalance in the NFI.Key words: forest cover-types, area proportions, stem volume, disturbance rates, ratio estimators. RÉSUMÉL'inventaire forestier national du Canada (NFI) est confronté à une problématique de déséquilibre dans les données de placettes photographiques de 400 ha disponibles pour évaluer l' état de la forêt et son changement. On envisage donc d'utiliser l'imputation multiple (MI) des données manquantes afin d'amoindrir le biais occasionné par le déséquilibre spatial et, dans une moindre mesure, améliorer la précision par rapport à ce qu'il est possible d'atteindre avec le sous-ensemble de placettes pour lesquelles nous disposons de données à jour. Dans cette étude, nous avons exploré les possibilités de MI avec les données provenant de trois sites d' étude situés dans les provinces de Québec, de l'Ontario et de la Saskatchewan. De façon plus particulière, nous nous sommes intéressés à l' état à temps T 2 et au changement survenu entre T 1 et T 2 dans les proportions des surfaces par type de couvert et le volume par tige par unité de surface. À chaque endroit nous avons observé des différences significatives pour ces paramètres à T 1 selon qu' on utilisait ou non les données de T 2 . Une procé-dure MI avec 20 répétitions d'imputations des données manquantes basées sur un modèle stochastique s' est avérée efficace pour atténuer le biais qui résulterait si l'inférence à T 2 reposait uniquement sur les placettes disposant des données à T 2 . Toutefois, les différences dans la technique de photo-interprétation à T 1 et à T 2 combinées à l'absence de stratification des placettes perturbées et intactes a masqué la majeure partie du g...

show abstract

The data storage and analysis system of the Swiss National Forest Inventory

Cited by 23 publications

References 19 publications

Multiple factors modulate tree growth complementarity in Central European mixed forests

Multiple factors modulate tree growth complementarity in Central European mixed forests

Understanding Forest Health with Remote Sensing, Part III: Requirements for a Scalable Multi-Source Forest Health Monitoring Network Based on Data Science Approaches

Updating Canada’s National Forest Inventory with multiple imputations of missing contemporary data

Contact Info

Product

Resources

About