You can access free preprints of all my non-open-access articles under following Dropbox folder. For manuscripts currently submitted or in review, please see my full CV.

Peer-reviewed papers

[16] Senf, C., Pflugmacher, D., Zhiqiang, Y., Sebald, S., Knorrn, J., Neumann, M., Hostert, P., and Seidl, R. (accepted) Canopy mortality has doubled across Europe’s temperate forests in the last three decades. Nature Communications.

[15] Sommerfeld, A., Senf, C., Buma, B., D’Amato, A. W., Després, T., Díaz-Hormazábal, I., Fraver, S., Frelich, L. E., Gutiérrez, Á. G., Hart, S. J., Harvey, B. J., He, H. S., Hlásny, T., Holz, A., Kitzberger, T., Kulakowski, D., Lindenmayer, D., Mori, A. S., Müller, J., Paritsis, J., Perry, G., Stephens, S., Svoboda, M., Turner, M. G., Veblen, T. T., and Seidl, R. (in press) Patterns and drivers of recent disturbances across the temperate forest biome. Both authors contributed equally. Nature Communications.

[14] Senf, C. and Seidl R. (2018) Natural disturbances are spatially diverse but temporally synchronized across temperate forest landscapes in Europe. Global Change Biology, 24(3), 1201-1211.

[13] Senf, C., Pflugmacher, D., Hostert, P. and Seidl, R. (2017) Using Landsat time series for characterizing forest disturbance dynamics in the coupled human and natural systems of Central Europe. ISPRS Journal of Photogrammetry and Remote Sensing, 130, 453-463.

[12] Oeser, J., Pflugmacher, D., Senf, C., Heurich, M. and Hostert, P. (2017) Using intra-annual Landsat time series for attributing forest disturbance agents in Central Europe. Forests, 8, 251.

[11] Senf, C., Seidl, R. and Hostert, P. (2017) Remote sensing of forest insect disturbances: current state and future directions. International Journal of Applied Earth Observation and Geo-information, 60, 49-60.

[10] Senf, C., Pflugmacher, D., Heurich, M. and Krueger T. (2017) A Bayesian hierarchical model for estimating spatial and temporal variation in vegetation phenology from Landsat time series. Remote Sensing of Environment, 194, 155-160.

[9] Leitão, J. P., Schwieder, M. and Senf, C. (2017) sgdm: an R package for performing sparse generalized dissimilarity modelling, including tools for gdm. ISPRS International Journal of Geo-Information, 6(1), 23-35.

[8] Senf, C., Campbell, E., Wulder, M. A., Pflugmacher, D. and Hostert P. (2017) A multi-scale analysis of western spruce budworm spatiotemporal outbreak patterns. Landscape Ecology, 32(3), 501-514.

[7] Kehoe, L., Senf, C., Meyer, C., Gerstner, K., Kreft, H. and Kuemmerle, T. (2017) Land cover and land-use intensity rival biomes in predicting global species richness. Ecography, 40, 1118-1128.

[6] Senf, C., Wulder, M. A., Campbell, E. and Hostert P. (2016) Using Landsat to assess the relationship between spatiotemporal patterns of western spruce budworm outbreaks and regional-scale weather variability. Canadian Journal of Remote Sensing, 42(6), 706-718.

[5] Senf, C., Pflugmacher, D., Wulder, M. A. and Hostert, P. (2015) Characterizing spectral-temporal patterns of defoliator and bark beetle disturbances using Landsat time series. Remote Sensing of Environment, 170, 166-177.

[4] Held, M., Rabe, A., Senf, C., van der Linden, S. and Hostert, P. (2015) Analyzing Hyperspectral and Hypertemporal Data by Decoupling Feature Redundancy and Feature Relevance. IEEE Geoscience and Remote Sensing Letters, 12(5), 983-987. 10.1109/LGRS.2014.2371242

[3] Senf, C., Leitão, J.P., Pflugmacher, D., van der Linden, S. and Hostert, P. (2015) Mapping land cover in complex Mediterranean landscapes using Landsat: Improved classification accuracies from integrating multi-seasonal and synthetic imagery. Remote Sensing of Environment, 156, 527-536.

[2] Schwieder, M., Leitão, J.P., Suess, S., Senf, C. and Hostert, P. (2014) Estimating fractional shrub cover using simulated EnMAP data: A comparison of three machine learning regression techniques. Remote Sensing, 6(4), 3427-3445.

[1] Senf, C., Pflugmacher, D., van der Linden, S. and Hostert, P. (2013) Mapping Rubber Plantations and Natural Forests in Xishuangbanna (Southwest China) Using Multi-Spectral Phenological Metrics from MODIS Time Series. Remote Sensing, 5(6), 2795-2812.