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1. Accuracy, realism and general applicability of European forest models

Author

Mahnken, M., Cailleret, M., Collalti, A., Trotta, C., Biondo, C., D’Andrea, E., Dalmonech, D., Marano, G., Mäkelä, A., Minunno, F., Peltoniemi, M., Trotsiuk, V., Nadal‐Sala, D., Sabaté, S., Vallet, P., Aussenac, R., Cameron, D.R., Bohn, Friedrich, Grote, R., Augustynczik, A.L.D., Yousefpour, R., Huber, N., Bugmann, H., Merganičová, K., Merganic, J., Valent, P., Lasch-Born, P., Hartig, F., Vega del Valle, I.D., Volkholz, J., Gutsch, M., Matteucci, G., Krejza, J., Ibrom, A., Meesenburg, H., Rötzer, T., van der Maaten-Theunissen, M., van der Maaten, E., Reyer, C.P.O., Mahnken, M., Cailleret, M., Collalti, A., Trotta, C., Biondo, C., D’Andrea, E., Dalmonech, D., Marano, G., Mäkelä, A., Minunno, F., Peltoniemi, M., Trotsiuk, V., Nadal‐Sala, D., Sabaté, S., Vallet, P., Aussenac, R., Cameron, D.R., Bohn, Friedrich, Grote, R., Augustynczik, A.L.D., Yousefpour, R., Huber, N., Bugmann, H., Merganičová, K., Merganic, J., Valent, P., Lasch-Born, P., Hartig, F., Vega del Valle, I.D., Volkholz, J., Gutsch, M., Matteucci, G., Krejza, J., Ibrom, A., Meesenburg, H., Rötzer, T., van der Maaten-Theunissen, M., van der Maaten, E., and Reyer, C.P.O.

Abstract

Forest models are instrumental for understanding and projecting the impact of climate change on forests. A considerable number of forest models have been developed in the last decades. However, few systematic and comprehensive model comparisons have been performed in Europe that combine an evaluation of modelled carbon and water fluxes and forest structure. We evaluate 13 widely-used, state-of-the-art, stand-scale forest models against field measurements of forest structure and eddy-covariance data of carbon and water fluxes over multiple decades across an environmental gradient at nine typical European forest stands. We test the models’ performance in three dimensions: accuracy of local predictions (agreement of modelled and observed annual data), realism of environmental responses (agreement of modelled and observed responses of daily gross primary productivity to temperature, radiation and vapor pressure deficit) and general applicability (proportion of European tree species covered). We find that multiple models are available that excel according to our three dimensions of model performance. For the accuracy of local predictions, variables related to forest structure have lower random and systematic errors than annual carbon and water flux variables. Moreover, the multi-model ensemble mean provided overall more realistic daily productivity responses to environmental drivers across all sites than any single individual model. The general applicability of the models is high, as almost all models are currently able to cover Europe’s common tree species. We show that forest models complement each other in their response to environmental drivers and that there are several cases in which individual models outperform the model ensemble. Our framework provides a first step to capturing essential differences between forest models that go beyond the most commonly used accuracy of predictions. Overall, this study provides a point of reference for future model work aimed at

Published
2022

4. The PROFOUND Database for evaluating vegetation models and simulating climate impacts on European forests

Author

Reyer, C.P.O., Silveyra Gonzalez, R., Dolos, K., Hartig, F., Hauf, Y., Noack, M., Lasch-Born, P., Rötzer, T., Pretzsch, H., Meesenburg, H., Fleck, S., Wagner, M., Bolte, A., Sanders, T.G.M., Kolari, P., Mäkelä, A., Vesala, T., Mammarella, I., Pumpanen, J., Collalti, A., Trotta, C., Matteucci, G., D'Andrea, E., Foltýnová, L., Krejza, J., Ibrom, A., Pilegaard, K., Loustau, D., Bonnefond, J.-M., Berbigier, P., Picart, D., Lafont, S., Dietzel, M., Cameron, D., Vieno, M., Tian, H., Palacios-Orueta, A., Cicuendez, V., Recuero, L., Wiese, K., Büchner, M., Lange, S., Volkholz, J., Kim, H., Horemans, J.A., Bohn, Friedrich, Steinkamp, J., Chikalanov, A., Weedon, G.P., Sheffield, J., Babst, F., Vega del Valle, I., Suckow, F., Martel, S., Mahnken, M., Gutsch, M., Frieler, K., Reyer, C.P.O., Silveyra Gonzalez, R., Dolos, K., Hartig, F., Hauf, Y., Noack, M., Lasch-Born, P., Rötzer, T., Pretzsch, H., Meesenburg, H., Fleck, S., Wagner, M., Bolte, A., Sanders, T.G.M., Kolari, P., Mäkelä, A., Vesala, T., Mammarella, I., Pumpanen, J., Collalti, A., Trotta, C., Matteucci, G., D'Andrea, E., Foltýnová, L., Krejza, J., Ibrom, A., Pilegaard, K., Loustau, D., Bonnefond, J.-M., Berbigier, P., Picart, D., Lafont, S., Dietzel, M., Cameron, D., Vieno, M., Tian, H., Palacios-Orueta, A., Cicuendez, V., Recuero, L., Wiese, K., Büchner, M., Lange, S., Volkholz, J., Kim, H., Horemans, J.A., Bohn, Friedrich, Steinkamp, J., Chikalanov, A., Weedon, G.P., Sheffield, J., Babst, F., Vega del Valle, I., Suckow, F., Martel, S., Mahnken, M., Gutsch, M., and Frieler, K.

Abstract

Process-based vegetation models are widely used to predict local and global ecosystem dynamics and climate change impacts. Due to their complexity, they require careful parameterization and evaluation to ensure that projections are accurate and reliable. The PROFOUND Database (PROFOUND DB) provides a wide range of empirical data on European forests to calibrate and evaluate vegetation models that simulate climate impacts at the forest stand scale. A particular advantage of this database is its wide coverage of multiple data sources at different hierarchical and temporal scales, together with environmental driving data as well as the latest climate scenarios. Specifically, the PROFOUND DB provides general site descriptions, soil, climate, CO2, nitrogen deposition, tree and forest stand level, and remote sensing data for nine contrasting forest stands distributed across Europe. Moreover, for a subset of five sites, time series of carbon fluxes, atmospheric heat conduction and soil water are also available. The climate and nitrogen deposition data contain several datasets for the historic period and a wide range of future climate change scenarios following the Representative Concentration Pathways (RCP2.6, RCP4.5, RCP6.0, RCP8.5). We also provide pre-industrial climate simulations that allow for model runs aimed at disentangling the contribution of climate change to observed forest productivity changes. The PROFOUND DB is available freely as a “SQLite” relational database or “ASCII” flat file version (at https://doi.org/10.5880/PIK.2020.006/; Reyer et al., 2020). The data policies of the individual contributing datasets are provided in the metadata of each data file. The PROFOUND DB can also be accessed via the ProfoundData R package (https://CRAN.R-project.org/package=ProfoundData; Silveyra Gonzalez et al., 2020), which provides basic functions to explore, plot and extract the data for model set-up, calibration and evaluation.

Published
2020

8. The Forest Observation System, building a global reference dataset for remote sensing of forest biomass [data paper]

Author

Schepaschenko, D., Chave, J., Phillips, O. L., Lewis, S. L., Davies, S. J., Réjou-Méchain, Maxime, Sist, P., Scipal, K., Perger, C., Herault, B., Labriere, N., Hofhansl, F., Affum-Baffoe, K., Aleinikov, A., Alonso, A., Amani, C., Araujo-Murakami, A., Armston, J., Arroyo, L., Ascarrunz, N., Azevedo, C., Baker, T., Balazy, R., Bedeau, C., Berry, N., Bilous, A. M., Bilous, S. Y., Bissiengou, P., Blanc, L., Bobkova, K. S., Braslavskaya, T., Brienen, R., Burslem, Dfrp, Condit, R., Cuni-Sanchez, A., Danilina, D., Torres, D. D., Derroire, G., Descroix, L., Sotta, E. D., d'Oliveira, M. V. N., Dresel, C., Erwin, T., Evdokimenko, M. D., Falck, J., Feldpausch, T. R., Foli, E. G., Foster, R., Fritz, S., Garcia-Abril, A. D., Gornov, A., Gornova, M., Gothard-Bassebe, E., Gourlet-Fleury, S., Guedes, M., Hamer, K. C., Susanty, F. H., Higuchi, N., Coronado, E. N. H., Hubau, W., Hubbell, S., Ilstedt, U., Ivanov, V. V., Kanashiro, M., Karlsson, A., Karminov, V. N., Killeen, T., Koffi, J. C. K., Konovalova, M., Kraxner, F., Krejza, J., Krisnawati, H., Krivobokov, L. V., Kuznetsov, M. A., Lakyda, I., Lakyda, P. I., Licona, J. C., Lucas, R. M., Lukina, N., Lussetti, D., Malhi, Y., Manzanera, J. A., Marimon, B., Martinez, R. V., Martynenko, O. V., Matsala, M., Matyashuk, R. K., Mazzei, L., Memiaghe, H., Mendoza, C., Mendoza, A. M., Moroziuk, O. V., Mukhortova, L., Musa, S., Nazimova, D. I., Okuda, T., Oliveira, L. C., Ontikov, P. V., Osipov, A. F., Pietsch, S., Playfair, M., Poulsen, J., Radchenko, V. G., Rodney, K., Rozak, A. H., Ruschel, A., Rutishauser, E., See, L., Shchepashchenko, M., Shevchenko, N., Shvidenko, A., Silveira, M., Singh, J., Sonke, B., Souza, C., Sterenczak, K., Stonozhenko, L., Sullivan, M. J. P., Szatniewska, J., Aedoumg, H. T., Ter Steege, H., Tikhonova, E., Toledo, M., Trefilova, O. V., Valbuena, R., Gamarra, L. V., Vasiliev, S., Vedrova, E. F., Verhovets, S. V., Vidal, E., Vladimirova, N. A., Vleminckx, J., Vos, V. A., Vozmitel, F. K., Wanek, W., West, T. A. P., Woell, H., Woods, J. T., Wortel, V., Yamada, T., Hajar, Z. S. N., and Zo-Bi, I. C.

Abstract

Forest biomass is an essential indicator for monitoring the Earth's ecosystems and climate. It is a critical input to greenhouse gas accounting, estimation of carbon losses and forest degradation, assessment of renewable energy potential, and for developing climate change mitigation policies such as REDD+, among others. Wall-to-wall mapping of aboveground biomass (AGB) is now possible with satellite remote sensing (RS). However, RS methods require extant, up-to-date, reliable, representative and comparable in situ data for calibration and validation. Here, we present the Forest Observation System (FOS) initiative, an international cooperation to establish and maintain a global in situ forest biomass database. AGB and canopy height estimates with their associated uncertainties are derived at a 0.25 ha scale from field measurements made in permanent research plots across the world's forests. All plot estimates are geolocated and have a size that allows for direct comparison with many RS measurements. The FOS offers the potential to improve the accuracy of RS- based biomass products while developing new synergies between the RS and ground-based ecosystem research communities.

Published
2019

10. A global reference dataset for remote sensing of forest biomass. The Forest Observation System approach

Author

Shchepashchenko, D., Chave, J., Phillips, O.L., Lewis, S.L., Davies, S.J., Réjou-Méchain, M., Sist, P., Scipal, K., Perger, C., Herault, B., Labrière, N., Hofhansl, F., Affum-Baffoe, K., Aleinikov, A., Alonso, A., Amani, C., Araujo-Murakami, A., Armston, J., Arroyo, L., Ascarrunz, N., Azevedo, C., Baker, T., Bałazy, R., Banki, O., Bedeau, C., Berry, N., Bilous, A.M., Bilous, S.Y., Bissiengou, P., Blanc, L., Bobkova, K.S., Braslavskaya, T., Brienen, R., Burslem, D., Condit, R., Cuni-Sanchez, A., Danilina, D., del Castillo Torres, D., Derroire, G., Descroix, L., Doff Sotta, E., d'Oliveira, M.V.N., Dresel, C., Erwin, T., Evdokimenko, M.D., Falck, J., Feldpausch, T.R., Foli, E.G., Foster, R., Fritz, S., Garcia-Abril, A.D., Gornov, A., Gornova, M., Gothard-Bassébé, E., Gourlet-Fleury, S., Guedes, M., Hamer, K., Susanty, F.H., Higuchi, N., Honorio Coronado, E.N., Hubau, W., Hubbell, S., Ilstedt, U., Ivanov, V., Kanashiro, M., Karlsson, A., Karminov, V.N., Killeen, T., Konan, J.K., Konovalova, M., Kraxner, F., Krejza, J., Krisnawati, H., Krivobokov, L.V., Kuznetsov, M.A., Lakyda, I., Lakyda, P.I., Licona, J.C., Lucas, R.M., Lukina, N., Lussetti, D., Malhi, Y., Manzanera, J.A., Marimon, B., Marimon Junior, B.H., Martinez, R.V., Martynenko, O.V., Matsala, M.S., Matyashuk, R.K., Mazzei, L., Memiaghe, H., Mendoza, C., Monteagudo-Mendoza, A., Morozyuk, O.V., Mukhortova, L., Musa, S., Nazimova, D.I., Okuda, T., Oliveira, L.C., Ontikov, P.V., Osipov, A.F., Gutierrez, A.P., Pietsch, S., Playfair, M., Poulsen, J., Radchenko, V., Rodney, K., Rozak, A.H., Ruschel, A., Rutishauser, E., See, L., Shchepashchenko, M., Shevchenko, N., Shvidenko, A., Silva-Espejo, J.E., Silveira, M., Singh, J., Sonké, B., Souza, C., Stereńczak, K., Sullivan, M.J.P., Szatniewska, J., Taedoumg, H., ter Steege, H., Tikhonova, E., Toledo, M., Trefilova, O.V., Valbuena, R., Valenzuela Gamarra, L.V., Vedrova, E.F., Verhovets, S.V., Vidal, E., Vladimirova, N.A., Vleminckx, J., Vos, V.A., Vozmitel, F.K., Wanek, W., West, T.A.P., Woell, H., Woods, J.T., Wortel, V., Yamada, T., Zamah Shari, N.H., Zo-Bi, I.C., Shchepashchenko, D., Chave, J., Phillips, O.L., Lewis, S.L., Davies, S.J., Réjou-Méchain, M., Sist, P., Scipal, K., Perger, C., Herault, B., Labrière, N., Hofhansl, F., Affum-Baffoe, K., Aleinikov, A., Alonso, A., Amani, C., Araujo-Murakami, A., Armston, J., Arroyo, L., Ascarrunz, N., Azevedo, C., Baker, T., Bałazy, R., Banki, O., Bedeau, C., Berry, N., Bilous, A.M., Bilous, S.Y., Bissiengou, P., Blanc, L., Bobkova, K.S., Braslavskaya, T., Brienen, R., Burslem, D., Condit, R., Cuni-Sanchez, A., Danilina, D., del Castillo Torres, D., Derroire, G., Descroix, L., Doff Sotta, E., d'Oliveira, M.V.N., Dresel, C., Erwin, T., Evdokimenko, M.D., Falck, J., Feldpausch, T.R., Foli, E.G., Foster, R., Fritz, S., Garcia-Abril, A.D., Gornov, A., Gornova, M., Gothard-Bassébé, E., Gourlet-Fleury, S., Guedes, M., Hamer, K., Susanty, F.H., Higuchi, N., Honorio Coronado, E.N., Hubau, W., Hubbell, S., Ilstedt, U., Ivanov, V., Kanashiro, M., Karlsson, A., Karminov, V.N., Killeen, T., Konan, J.K., Konovalova, M., Kraxner, F., Krejza, J., Krisnawati, H., Krivobokov, L.V., Kuznetsov, M.A., Lakyda, I., Lakyda, P.I., Licona, J.C., Lucas, R.M., Lukina, N., Lussetti, D., Malhi, Y., Manzanera, J.A., Marimon, B., Marimon Junior, B.H., Martinez, R.V., Martynenko, O.V., Matsala, M.S., Matyashuk, R.K., Mazzei, L., Memiaghe, H., Mendoza, C., Monteagudo-Mendoza, A., Morozyuk, O.V., Mukhortova, L., Musa, S., Nazimova, D.I., Okuda, T., Oliveira, L.C., Ontikov, P.V., Osipov, A.F., Gutierrez, A.P., Pietsch, S., Playfair, M., Poulsen, J., Radchenko, V., Rodney, K., Rozak, A.H., Ruschel, A., Rutishauser, E., See, L., Shchepashchenko, M., Shevchenko, N., Shvidenko, A., Silva-Espejo, J.E., Silveira, M., Singh, J., Sonké, B., Souza, C., Stereńczak, K., Sullivan, M.J.P., Szatniewska, J., Taedoumg, H., ter Steege, H., Tikhonova, E., Toledo, M., Trefilova, O.V., Valbuena, R., Valenzuela Gamarra, L.V., Vedrova, E.F., Verhovets, S.V., Vidal, E., Vladimirova, N.A., Vleminckx, J., Vos, V.A., Vozmitel, F.K., Wanek, W., West, T.A.P., Woell, H., Woods, J.T., Wortel, V., Yamada, T., Zamah Shari, N.H., and Zo-Bi, I.C.

Abstract

Forest biomass is an essential indicator for monitoring the Earth’s ecosystems and climate. It is a critical input to greenhouse gas accounting, estimation of carbon losses and forest degradation, assessment of renewable energy potential, and for developing climate change mitigation policies such as REDD+, among others. Wall-to-wall mapping of aboveground biomass (AGB) is now possible with satellite remote sensing (RS). However, RS methods require extant, up-to-date, reliable, representative and comparable in situ data for calibration and validation. Here, we present the Forest Observation System (FOS) initiative, an international cooperation to establish and maintain a global in situ forest biomass database. AGB and canopy height estimates with their associated uncertainties are derived at a 0.25ha scale from field measurements made in permanent research plots across the world's forests. All plot estimates are geolocated and have a size that allows for direct comparison with many RS measurements. The FOS offers the potential to improve the accuracy of RS-based biomass products while developing new synergies between the RS and ground-based ecosystem research communities. Live, most up-to-date dataset is available at https://forest-observation-system.net

Published
2019

11. The Forest Observation System, building a global reference dataset for remote sensing of forest biomass

Author

Schepaschenko, D., Chave, J., Phillips, O.L., Lewis, S.L., Davies, S.J., Réjou-Méchain, M., Sist, P., Scipal, K., Perger, C., Herault, B., Labrière, N., Hofhansl, F., Affum-Baffoe, K., Aleinikov, A., Alonso, A., Amani, C., Araujo-Murakami, A., Armston, J., Arroyo, L., Ascarrunz, N., Azevedo, C., Baker, T., Bałazy, R., Bedeau, C., Berry, N., Bilous, A.M., Bilous, S., Bissiengou, P., Blanc, L., Bobkova, .S., Braslavskaya, T., Brienen, R., Burslem, D., Condit, R., Cuni-Sanchez, A., Danilina, D., del Castillo Torres, D., Derroire, G., Descroix, L., Sotta, E.D., d’Oliveira, M.V.N., Dresel, C., Erwin, T., Evdokimenko, M.D., Falck, J., Feldpausch, T.R., Foli, E.G., Foster, R., Fritz, S., Garcia-Abril, A.D., Gornov, A., Gornova, M., Gothard-Bassébé, E., Gourlet-Fleury, S., Guedes, M., Hamer, K.C., Susanty, F.H., Higuchi, N., Coronado, E.N.H., Hubau, W., Hubbell, S., Ilstedt, U., Ivanov, V.V., Kanashiro, M., Karlsson, A., Karminov, V.N., Killeen, T., Koffi, J.-C., Konovalova, M., Kraxner, F., Krejza, J., Krisnawati, H., Krivobokov, L.V., Kuznetsov, M.A., Lakyda, I., Lakyda, P.I., Licona, J.C., Lucas, R.M., Lukina, N., Lussetti, D., Malhi, Y., Manzanera, J.A., Marimon, B., Marimon, B.H., Martinez, R.V., Martynenko, O.V., Matsala, M., Matyashuk, R.K., Mazzei, L., Memiaghe, H., Mendoza, C., Mendoza, A.M., Moroziuk, Olga V., Mukhortova, L., Musa, S., Nazimova, D.I., Okuda, T., Oliveira, L.C., Ontikov, P.V., Osipov, A., Pietsch, S., Playfair, M., Poulsen, J., Radchenko, V.G., Rodney, K., Rozak, A.H., Ruschel, A., Rutishauser, E., See, L., Shchepashchenko, M., Shevchenko, N., Shvidenko, A., Silveira, M., Singh, J., Sonké, B., Souza, C., Stereńczak, K., Stonozhenko, L., Sullivan, M., Szatniewska, J., Taedoumg, H., ter Steege, H., Tikhonova, E., Toledo, M., Trefilova, O.V., Valbuena, R., Gamarra, L.V., Vasiliev, S., Vedrova, E.F., Verhovets, S.V., Vidal, E., Vladimirova, N.A., Vleminckx, J., Vos, V.A., Vozmitel, F.K., Wanek, W., West, T., Woell, H., Woods, J.T., Wortel, V., Yamada, T., Nur Hajar, Z., Zo-Bi, I., Schepaschenko, D., Chave, J., Phillips, O.L., Lewis, S.L., Davies, S.J., Réjou-Méchain, M., Sist, P., Scipal, K., Perger, C., Herault, B., Labrière, N., Hofhansl, F., Affum-Baffoe, K., Aleinikov, A., Alonso, A., Amani, C., Araujo-Murakami, A., Armston, J., Arroyo, L., Ascarrunz, N., Azevedo, C., Baker, T., Bałazy, R., Bedeau, C., Berry, N., Bilous, A.M., Bilous, S., Bissiengou, P., Blanc, L., Bobkova, .S., Braslavskaya, T., Brienen, R., Burslem, D., Condit, R., Cuni-Sanchez, A., Danilina, D., del Castillo Torres, D., Derroire, G., Descroix, L., Sotta, E.D., d’Oliveira, M.V.N., Dresel, C., Erwin, T., Evdokimenko, M.D., Falck, J., Feldpausch, T.R., Foli, E.G., Foster, R., Fritz, S., Garcia-Abril, A.D., Gornov, A., Gornova, M., Gothard-Bassébé, E., Gourlet-Fleury, S., Guedes, M., Hamer, K.C., Susanty, F.H., Higuchi, N., Coronado, E.N.H., Hubau, W., Hubbell, S., Ilstedt, U., Ivanov, V.V., Kanashiro, M., Karlsson, A., Karminov, V.N., Killeen, T., Koffi, J.-C., Konovalova, M., Kraxner, F., Krejza, J., Krisnawati, H., Krivobokov, L.V., Kuznetsov, M.A., Lakyda, I., Lakyda, P.I., Licona, J.C., Lucas, R.M., Lukina, N., Lussetti, D., Malhi, Y., Manzanera, J.A., Marimon, B., Marimon, B.H., Martinez, R.V., Martynenko, O.V., Matsala, M., Matyashuk, R.K., Mazzei, L., Memiaghe, H., Mendoza, C., Mendoza, A.M., Moroziuk, Olga V., Mukhortova, L., Musa, S., Nazimova, D.I., Okuda, T., Oliveira, L.C., Ontikov, P.V., Osipov, A., Pietsch, S., Playfair, M., Poulsen, J., Radchenko, V.G., Rodney, K., Rozak, A.H., Ruschel, A., Rutishauser, E., See, L., Shchepashchenko, M., Shevchenko, N., Shvidenko, A., Silveira, M., Singh, J., Sonké, B., Souza, C., Stereńczak, K., Stonozhenko, L., Sullivan, M., Szatniewska, J., Taedoumg, H., ter Steege, H., Tikhonova, E., Toledo, M., Trefilova, O.V., Valbuena, R., Gamarra, L.V., Vasiliev, S., Vedrova, E.F., Verhovets, S.V., Vidal, E., Vladimirova, N.A., Vleminckx, J., Vos, V.A., Vozmitel, F.K., Wanek, W., West, T., Woell, H., Woods, J.T., Wortel, V., Yamada, T., Nur Hajar, Z., and Zo-Bi, I.

Abstract

Forest biomass is an essential indicator for monitoring the Earth’s ecosystems and climate. It is a critical input to greenhouse gas accounting, estimation of carbon losses and forest degradation, assessment of renewable energy potential, and for developing climate change mitigation policies such as REDD+, among others. Wall-to-wall mapping of aboveground biomass (AGB) is now possible with satellite remote sensing (RS). However, RS methods require extant, up-to-date, reliable, representative and comparable in situ data for calibration and validation. Here, we present the Forest Observation System (FOS) initiative, an international cooperation to establish and maintain a global in situ forest biomass database. AGB and canopy height estimates with their associated uncertainties are derived at a 0.25 ha scale from field measurements made in permanent research plots across the world’s forests. All plot estimates are geolocated and have a size that allows for direct comparison with many RS measurements. The FOS offers the potential to improve the accuracy of RS-based biomass products while developing new synergies between the RS and ground-based ecosystem research communities.

Published
2019

15. [Analysis of intracranial pressure signals using artificial neural networks]

Author

Mariak Z, Miroslaw SWIERCZ, Krejza J, Lewko J, and Lyson T

Subjects
Intracranial Pressure, Predictive Value of Tests, Electrodiagnosis, Models, Neurological, Humans, Signal Processing, Computer-Assisted, Neural Networks, Computer, Intracranial Hypertension, Algorithms
Abstract

Intracranial pressure (ICP) is influenced by an array of predictable and unpredictable factors. Statistical modelling of this signal has only limited applicability because of the significant load of stochastic components. We tested the efficiency of an alternative approach, based on the methodology of artificial neural networks (ANNs) in the on-line prediction of future values of ICP and in the classification of signal properties. Satisfactory accuracy of forecasting was achieved with the ANNs for a 3-minute prediction horizon, while the prediction quality with autoregressive models of statistical origin was proved unsatisfactory. The results obtained with the ANNs were further improved when signal pre-processing with wavelet transform was employed. Nevertheless, even with the ANN methodology, no sudden breakdowns in the ICP signal (which in this respect might be compared to a "catastrophe") can be forecast with any practical applicability. We therefore applied two ANN algorithms, oriented at classification and discrimination of the global properties of the ICP signal. The neural network was expected to discriminate those sets of signal properties, which were assumed to correspond to certain clinical conditions of the patient. In a "dynamic pattern classification" the network was presented with several sections of ICP records. This was combined with information about the assignment of a given record to one of four arbitrary classes of danger. In this mode no data pre-processing was carried out, in contrast to our second approach, in which the signal was pre-processed with statistical analyses and only these intermediate coefficients were fed to the ANN classifier. The results obtained with both classification methods at their present stage of training were similar and approximated to a 70% rate of judgements consistent with expert scoring. Nevertheless, the method based on the assessment of global parameters of the ICP record seems more promising, because it leaves the possibility of extending the set of training data by information from other diagnostic modalities. The study aims towards the development of a pseudo-intelligent computer expert system, which has would be taught salient links between data extracted from the ICP signal and higher- order data, which contributed to the expert score. Hence the system would be able to make decisions on the basis of a reduced set of input information, available from a standard monitoring modality.

Published
2001

40. Cerebrovascular Reactivity across the Menstrual Cycle in Young Healthy Women.

Author

KREJZA, J., RUDZINSKI, W., ARKUSZEWSKI, M., ONUOHA, O., and MELHEM, E. R.

Abstract

This study evaluated the relationship of cerebrovascular reactivity in young healthy women with changes in concentrations of circulating ovarian hormones throughout the menstrual cycle. Nineteen healthy nulliparous, right-handed, regularly menstruating women (age 23-25 years) underwent color-coded duplex sonography of the common (CCA), internal (ICA) and external (ECA) carotid arteries on both sides. Mean blood flow velocity values measured before and ten minutes after intravenous administration of 1000 mg acetazolamide (ACE) were assessed in relation to the serum concentration of estrogen and progesterone on days 5, 13 and 26 of the cycle. After ACE ad­ministration flow velocity in the right CCA and ICA increased by 23% and 35% on day 5, 12% and 31% on day 13 and 30% and 47% on day 26 respectively, and the changes were significantly larger on the right side (F=6.793 and F=4.098 respectively; both p<0.05). Changes in blood flow velocity in the right CCA and ICA after ACE injection were significantly associated with ovarian hormone concentrations (F=3.828, P=0.028 and F=3.671, P=0.032 respectively). We conclude that cerebrov­ascular reactivity changes across the menstrual cycle are associated with ovarian steroid hormone changes, and are asymmetric. The results imply that vasculature of the right hemisphere may un­dergo cyclic vasodilation across the menstrual cycle and this effect should be considered in studies of cerebrovascular reactivity in women with migraine and mood disorders. [ABSTRACT FROM AUTHOR]

Published
2013
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41. Errata Corrige. Multiple Sclerosis Lesions in the Brain: Computer-Assisted Assessment of Lesion Load Dynamics on 3D FLAIR MR Images.

Author

Bilello, M., Arkuszewski, M., Nasrallah, I., Wu, X., Erus, G., and Krejza, J.

Abstract

The detection and monitoring of brain lesions caused by multiple sclerosis is commonly performed with the use of magnetic resonance imaging. Analysis of a large number of images is a time-consuming challenge to the neuroradiologist, that can be accelerated with the assistance of computer-detection software. In 98 baseline and follow-up brain magnetic resonance studies from 88 patients with a diagnosis of multiple sclerosis, we employed locally developed lesion-detection software to assess temporal change in the load of brain lesions and compared its results to routine clinical reports. Analyzing the differences between the follow-up study and the baseline study, the software displays the results in the form of a scrollable axial volume, with the changed lesions highlighted in different colors and superimposed on the baseline reference scan. Although disagreements between the software and the clinical readers in the detection of changed lesions were observed only in 12 (12.2%) cases, the difference reached statistical significance (p=0.04). The mean interpretation time with assistance of the software was 2.7±2.2 minutes. We conclude that the performance of the software-assisted interpretation in the analysis of change over time in multiple sclerosis brain lesions is different from the performance of clinical readers, with a possibly shorter assessment time. The software detected more changes from baseline than clinical readers, suggesting a higher sensitivity, which will have to be confirmed on further analysis. [ABSTRACT FROM AUTHOR]

Published
2012

46. Transcranial Colour Doppler Ultrasonography: State of the Art

Author

Krejza, J.

Abstract

Transcranial colour Doppler ultrasonography (TCCD), being a non-invasive method is an invaluable source of information on cerebral haemodynamics. It is the blood flow disturbances that often lead to irreversible ischemic lesions, which can then be detected a posterioriwith various imaging techniques. From a therapy point of view, diagnosis after infarction has obvious limitations. Hence, the importance of transcranial colour Doppler ultrasonography, which can be used at the bedside and even in restless patients. The imaging of intracranial structures, however, is inferior to that of computed tomography and magnetic resonance. Nonetheless, TCCD ultrasonography is becoming a widely accepted tool in the diagnosis of acute and chronic ischemic stroke and cerebral vasospasm following subarachnoid haemorrhage. Rapid detection of critical impairment of cerebral blood flow in a neurosurgical patient could guide further management. Large and medium size arteriovenous malformations can also be evaluated with this method. Future developments of TCCD ultrasonography would make the method less operator-dependent and more reliable in obtaining information on cerebral circulation.

Published
2001
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47. Transcranial Colour Doppler Sonography in the Management of a Patient with Spontaneous Intracranial Haematoma: A Case Report

Author

Krejza, J., Mariak, Z., Piekarskp, P., Lewko, J., and Tarasów, E.

Abstract

We present a case which demonstrates the merits of transcranial colour Doppler sonography (TCCD) in the diagnosis and monitoring of patients with spontaneous intracerebral haematoma. With this method it was possible to follow up disturbances of cerebral circulation, which allowed for the identification of critical states early enough to make decisions as to neurosurgical intervention. It should be emphasized that TCCD enabled us to discriminate between conditions in which the cerebral circulation was efficiently maintained and those in which it was insufficient, whilst increases in intracranial pressure and CT findings were similar in both situations. It appears that TCCD could be helpful in the detection of both hyperaemia and cerebral vasospasm.

Published
2001
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