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Assessing forest availability for wood supply in Europe
Alberdi, I., Bender, S., Riedel, T., Avitable, V., Boriaud, O., Bosela, M., … Hernández, L. (2020). Assessing forest availability for wood supply in Europe. Forest Policy and Economics, 111, 102032 (14 pp.). https://doi.org/10.1016/j.forpol.2019.102032
Airborne-laser-scanning-derived auxiliary information discriminating between broadleaf and conifer trees improves the accuracy of models for predicting timber volume in mixed and heterogeneously structured forests
Bont, L. G., Hill, A., Waser, L. T., Bürgi, A., Ginzler, C., & Blattert, C. (2020). Airborne-laser-scanning-derived auxiliary information discriminating between broadleaf and conifer trees improves the accuracy of models for predicting timber volume in mixed and heterogeneously structured forests. Forest Ecology and Management, 459, 117856 (18 pp.). https://doi.org/10.1016/j.foreco.2019.117856
Nitrogen deposition is the most important environmental driver of growth of pure, even-aged and managed European forests
Etzold, S., Ferretti, M., Reinds, G. J., Solberg, S., Gessler, A., Waldner, P., … de Vries, W. (2020). Nitrogen deposition is the most important environmental driver of growth of pure, even-aged and managed European forests. Forest Ecology and Management, 458, 117762 (13 pp.). https://doi.org/10.1016/j.foreco.2019.117762
Verbisseinfluss in der Tannenwaldstufe
Kupferschmid, A. D., & Gmür, P. A. (2020). Verbisseinfluss in der Tannenwaldstufe. Jagd und Natur, (1), 12-17.
Maximizing the likelihood of detecting outbreaks in temporal networks
Sterchi, M., Sarasua, C., Grütter, R., & Bernstein, A. (2020). Maximizing the likelihood of detecting outbreaks in temporal networks. In H. Cherifi, S. Gaito, J. F. Mendes, E. Moro, & L. M. Rocha (Eds.), Studies in computational intelligence: Vol. 882. Complex networks and their applications VIII. Volume 2 proceedings of the eighth international conference on complex networks and their applications COMPLEX NETWORKS 2019 (pp. 481-493). https://doi.org/10.1007/978-3-030-36683-4_39
Contrasting patterns of natural mortality in primary <em>Picea</em> forests of the Carpathian Mountains
Synek, M., Janda, P., Mikoláš, M., Nagel, T. A., Schurman, J. S., Pettit, J. L., … Svoboda, M. (2020). Contrasting patterns of natural mortality in primary Picea forests of the Carpathian Mountains. Forest Ecology and Management, 457, 117734 (9 pp.). https://doi.org/10.1016/j.foreco.2019.117734
Importance of tree species size dominance and heterogeneity on the productivity of spruce-fir-beech mountain forest stands in Europe
Torresan, C., del Río, M., Hilmers, T., Notarangelo, M., Bielak, K., Binder, F., … Pretzsch, H. (2020). Importance of tree species size dominance and heterogeneity on the productivity of spruce-fir-beech mountain forest stands in Europe. Forest Ecology and Management, 457, 117716 (12 pp.). https://doi.org/10.1016/j.foreco.2019.117716
LiDAR data as a proxy for light availability improve distribution modelling of woody species
Wüest, R. O., Bergamini, A., Bollmann, K., & Baltensweiler, A. (2020). LiDAR data as a proxy for light availability improve distribution modelling of woody species. Forest Ecology and Management, 456, 117644 (10 pp.). https://doi.org/10.1016/j.foreco.2019.117644
Coordination of the Swiss National Forest INventory Data Analysis System
Abegg, M. (2019). Coordination of the Swiss National Forest INventory Data Analysis System. In C. Fischer & B. Traub (Eds.), Managing forest ecosystems: Vol. 35. Swiss National Forest Inventory – Methods and models of the fourth assessment (pp. 405-415). https://doi.org/10.1007/978-3-030-19293-8_24
Estimating the mean direction of strongly dependent circular time series
Beran, J., & Ghosh, S. (2019). Estimating the mean direction of strongly dependent circular time series. Journal of Time Series Analysis. https://doi.org/10.1111/jtsa.12500
On aggregation of strongly dependent time series
Beran, J., Liu, H., & Ghosh, S. (2019). On aggregation of strongly dependent time series. Scandinavian Journal of Statistics. https://doi.org/10.1111/sjos.12421
Positive Beispiele der Wald-Wild-Situation bei der Weisstannenverjüngung
Bischof, T., Thormann, J. J., Gysin Zbinden, K., & Kupferschmid, A. D. (2019). Positive Beispiele der Wald-Wild-Situation bei der Weisstannenverjüngung. Schweizerische Zeitschrift für Forstwesen, 170(3), 142-152. https://doi.org/10.3188/szf.2019.0142
Raw data collection software in the Swiss NFI
Bischof, S., Stüdeli-Fey, E., & Meile, R. (2019). Raw data collection software in the Swiss NFI. In C. Fischer & B. Traub (Eds.), Managing forest ecosystems: Vol. 35. Swiss National Forest Inventory – Methods and models of the fourth assessment (pp. 377-401). https://doi.org/10.1007/978-3-030-19293-8_23
A QGIS plugin for optimized cable road layout planning
Bont, L., Moll, P., Ramstein, L., Heinimann, H. R., & Knobloch, C. (2019). A QGIS plugin for optimized cable road layout planning. Presented at the FORMEC 2019 – Exceeding the vision: forest mechanisation of the future. Sopron, Hungary.
Automated cable road layout and harvesting planning for multiple objectives in steep terrain
Bont, L. G., Maurer, S., & Breschan, J. R. (2019). Automated cable road layout and harvesting planning for multiple objectives in steep terrain. Forests, 10(8), 687 (26 pp.). https://doi.org/10.3390/f10080687
Bergwälder auf dem Weg zu neuer Wildnis
Brang, P. (2019). Bergwälder auf dem Weg zu neuer Wildnis. In F. Ebner (Ed.), Ich möchte meine Berge sehen. Von der Vielfalt und Schönheit der Alpen (pp. 117-145). Bern: Haupt.
Model complexity affects species distribution projections under climate change
Brun, P., Thuiller, W., Chauvier, Y., Pellissier, L., Wüest, R. O., Wang, Z., & Zimmermann, N. E. (2019). Model complexity affects species distribution projections under climate change. Journal of Biogeography. https://doi.org/10.1111/jbi.13734
Swiss NFI at a Glance
Brändli, U. B., & Hägeli, M. (2019). Swiss NFI at a Glance. In C. Fischer & B. Traub (Eds.), Managing forest ecosystems: Vol. 35. Swiss National Forest Inventory – Methods and models of the fourth assessment (pp. 3-35). https://doi.org/10.1007/978-3-030-19293-8_1
Bioenergy hotspots combined with socio-economic analysis: an important step to support the future energy transition
Burg, V., Bowman, G., Mohr, L., Trutnevyte, E., & Thees, O. (2019). Bioenergy hotspots combined with socio-economic analysis: an important step to support the future energy transition. Presented at the SCCER BIOSWEET annual conference. Lucerne, Switzerland.
Long-term wet bioenergy resources in Switzerland: drivers and projections until 2050
Burg, V., Bowman, G., Hellweg, S., & Thees, O. (2019). Long-term wet bioenergy resources in Switzerland: drivers and projections until 2050. Energies, 12(18), 3585 (21 pp.). https://doi.org/10.3390/en12183585
 

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