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2. Deriving regional pedotransfer functions to estimate soil bulk density in Austria

Author

Foldal Cecilie, Jandl Robert, Bohner Andreas, and Berger Ambros

Subjects
land-use, regional calibration, multiple regression, monte carlo, cross validation, landnutzung, regionale kalibrierung, kreuzvalidierung, Environmental sciences, GE1-350
Abstract

Soil bulk density is a required variable for quantifying stocks of elements in soils and is therefore instrumental for the evaluation of land-use related climate change mitigation measures. Our motivation was to derive a set of pedotransfer functions for soil bulk densities usable to accommodate different levels of data availabilities. We derived sets of linear equations for bulk density that are appropriate for different forms of land-use. After introducing uncertainty factors for measured parameters, we ran the linear models repeatedly in a Monte Carlo simulation in order to test the impact of inaccuracy. The reliability of the models was evaluated by a cross-validation. The single best predictor of soil bulk density is the content of soil organic carbon, yielding estimates with an adjusted R2 of approximately 0.5. A slight improvement of the estimate is possible when additionally, soil texture and soil depth are known. Residual analysis advocated the derivation of land-use specific models. Using transformed variables and assessing land-use specific pedotransfer functions, the determination coefficient (adjusted R2) of the multiple linear models ranged from 0.43 in cropland up to 0.65 for grassland soils. Compared to pedotransfer function, from the literature, the performance of the linear modes were similar but more accurate. Taking into account the likely inaccuracies when measuring soil organic carbon, the soil bulk density can be estimated with an accuracy of +/− 9 to 25% depending on land-use. We recommend measuring soil bulk density by standardized sampling of undisturbed soil cores, followed by post-processing of the samples in the lab by internationally harmonized protocols. Our pedotransfer functions are accurately and transparently presented, and derived from well-documented and high-quality soil data sets. We therefore consider them particularly useful in Austria, where the measured values for soil bulk densities are not available.

Published
2021
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3. Evaluating Tree Species Mapping: Probability Sampling Validation of Pure and Mixed Species Classes Using Convolutional Neural Networks and Sentinel-2 Time Series.

Author

Schadauer, Tobias, Karel, Susanne, Loew, Markus, Knieling, Ursula, Kopecky, Kevin, Bauerhansl, Christoph, Berger, Ambros, Graeber, Stephan, and Winiwarter, Lukas

Subjects
THEMATIC maps, CONVOLUTIONAL neural networks, FOREST biodiversity, REMOTE-sensing images, FOREST surveys
Abstract

The accurate large-scale classification of tree species is crucial for the monitoring, protection, and management of the Earth's invaluable forest ecosystems. Numerous previous studies have recognized the suitability of satellite imagery, particularly Sentinel-2 imagery, for this task. In this study, we utilized a dense phenology Sentinel-2 time series, which offered consistent data across multiple granules, to map tree species across the entire forested area in Austria. Aiming for the classification scheme to more accurately represent actual forest conditions, we included mixed tree species and sparsely populated classes (classes with sparse canopy cover) alongside pure tree species classes. To enhance the training data for the mixed and sparse classes, synthetic data creation was employed. Autocorrelation has significant implications for the validation of thematic maps. To investigate the impact of spatial dependency on validation data, two methods were employed at numerous split and buffer distances: spatial split validation and a validation method based on a buffered ground reference probability samples provided by the National Forest inventory (NFI). While a random training data holdout set yielded 99% accuracy, the spatial split validation resulted in 74% accuracy, emphasizing the importance of accounting for spatial autocorrelation when validating with holdout sets derived from polygon-based training data. The validation based on NFI data resulted in 55% overall accuracy, 91% post-hoc pure class accuracy, and 79% accuracy when confusions in phenological proximity were disregarded (e.g., spruce–larch confused with spruce). The significant differences in accuracy observed between spatial split and NFI validation underscore the challenge for polygon-based training data to capture ground reference forest complexity, particularly in areas with diverse forests. This hardship is further accentuated by the pure class accuracy of 91%, revealing the substantial impact of mixed stands on the accuracy of tree species maps. [ABSTRACT FROM AUTHOR]

Published
2024
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4. Austria

Author

Gschwantner, Thomas, Berger, Ambros, Büchsenmeister, Richard, Hauk, Elmar, Vidal, Claude, editor, Alberdi, Iciar A., editor, Hernández Mateo, Laura, editor, and Redmond, John J., editor

Published
2016
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9. Correction to: Harmonised projections of future forest resources in Europe

Author

Vauhkonen, Jari, primary, Berger, Ambros, additional, Gschwantner, Thomas, additional, Schadauer, Klemens, additional, Lejeune, Philippe, additional, Perin, Jérôme, additional, Pitchugin, Mikhail, additional, Adolt, Radim, additional, Zeman, Miroslav, additional, Johannsen, Vivian Kvist, additional, Kepfer-Rojas, Sebastian, additional, Sims, Allan, additional, Bastick, Claire, additional, Morneau, François, additional, Colin, Antoine, additional, Bender, Susann, additional, Kovácsevics, Pál, additional, Solti, György, additional, Kolozs, László, additional, Nagy, Dóra, additional, Nagy, Kinga, additional, Twomey, Mark, additional, Redmond, John, additional, Gasparini, Patrizia, additional, Notarangelo, Monica, additional, Rizzo, Maria, additional, Makovskis, Kristaps, additional, Lazdins, Andis, additional, Lupikis, Ainars, additional, Kulbokas, Gintaras, additional, Antón-Fernández, Clara, additional, Rego, Francisco Castro, additional, Nunes, Leónia, additional, Marin, Gheorghe, additional, Calota, Catalin, additional, Pantić, Damjan, additional, Borota, Dragan, additional, Roessiger, Joerg, additional, Bosela, Michal, additional, Šebeň, Vladimír, additional, Skudnik, Mitja, additional, Adame, Patricia, additional, Alberdi, Iciar, additional, Cañellas, Isabel, additional, Lind, Torgny, additional, Trubins, Renats, additional, Thürig, Esther, additional, Stadelmann, Golo, additional, Ditchburn, Ben, additional, Ross, David, additional, Gilbert, Justin, additional, Halsall, Lesley, additional, Lier, Markus, additional, and Packalen, Tuula, additional

Published
2019
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10. Harmonised projections of future forest resources in Europe

Author

Vauhkonen, Jari, primary, Berger, Ambros, additional, Gschwantner, Thomas, additional, Schadauer, Klemens, additional, Lejeune, Philippe, additional, Perin, Jérôme, additional, Pitchugin, Mikhail, additional, Adolt, Radim, additional, Zeman, Miroslav, additional, Johannsen, Vivian Kvist, additional, Kepfer-Rojas, Sebastian, additional, Sims, Allan, additional, Bastick, Claire, additional, Morneau, François, additional, Colin, Antoine, additional, Bender, Susann, additional, Kovácsevics, Pál, additional, Solti, György, additional, Kolozs, László, additional, Nagy, Dóra, additional, Nagy, Kinga, additional, Twomey, Mark, additional, Redmond, John, additional, Gasparini, Patrizia, additional, Notarangelo, Monica, additional, Rizzo, Maria, additional, Makovskis, Kristaps, additional, Lazdins, Andis, additional, Lupikis, Ainars, additional, Kulbokas, Gintaras, additional, Antón-Fernández, Clara, additional, Rego, Francisco Castro, additional, Nunes, Leónia, additional, Marin, Gheorghe, additional, Calota, Catalin, additional, Pantić, Damjan, additional, Borota, Dragan, additional, Roessiger, Joerg, additional, Bosela, Michal, additional, Šebeň, Vladimír, additional, Skudnik, Mitja, additional, Adame, Patricia, additional, Alberdi, Iciar, additional, Cañellas, Isabel, additional, Lind, Torgny, additional, Trubins, Renats, additional, Thürig, Esther, additional, Stadelmann, Golo, additional, Ditchburn, Ben, additional, Ross, David, additional, Gilbert, Justin, additional, Halsall, Lesley, additional, Lier, Markus, additional, and Packalen, Tuula, additional

Published
2019
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11. Harmonised projections of future forest resources in Europe

Author

Vauhkonen, Jari, Berger, Ambros, Gschwantner, Thomas, Schadauer, Klemens, Lejeune, Philippe, Perin, Jerome, Pitchugin, Mikhail, Adolt, Radim, Zeman, Miroslav, Johannsen, Vivian Kvist, Kepfer-Rojas, Sebastian, Sims, Allan, Bastick, Claire, Morneau, Francois, Colin, Antoine, Bender, Susann, Kovacsevics, Pal, Solti, Gyorgy, Kolozs, Laszlo, Nagy, Dora, Nagy, Kinga, Twomey, Mark, Redmond, John, Gasparini, Patrizia, Notarangelo, Monica, Rizzo, Maria, Makovskis, Kristaps, Lazdins, Andis, Lupikis, Ainars, Kulbokas, Gintaras, Anton-Fernandez, Clara, Rego, Francisco Castro, Nunes, Leonia, Marin, Gheorghe, Calota, Catalin, Pantic, Damjan, Borota, Dragan, Roessiger, Joerg, Bosela, Michal, Seben, Vladimir, Skudnik, Mitja, Adame, Patricia, Alberdi, Iciar, Canellas, Isabel, Lind, Torgny, Trubins, Renats, Thurig, Esther, Stadelmann, Golo, Ditchburn, Ben, Ross, David, Gilbert, Justin, Halsall, Lesley, Lier, Markus, Packalen, Tuula, Vauhkonen, Jari, Berger, Ambros, Gschwantner, Thomas, Schadauer, Klemens, Lejeune, Philippe, Perin, Jerome, Pitchugin, Mikhail, Adolt, Radim, Zeman, Miroslav, Johannsen, Vivian Kvist, Kepfer-Rojas, Sebastian, Sims, Allan, Bastick, Claire, Morneau, Francois, Colin, Antoine, Bender, Susann, Kovacsevics, Pal, Solti, Gyorgy, Kolozs, Laszlo, Nagy, Dora, Nagy, Kinga, Twomey, Mark, Redmond, John, Gasparini, Patrizia, Notarangelo, Monica, Rizzo, Maria, Makovskis, Kristaps, Lazdins, Andis, Lupikis, Ainars, Kulbokas, Gintaras, Anton-Fernandez, Clara, Rego, Francisco Castro, Nunes, Leonia, Marin, Gheorghe, Calota, Catalin, Pantic, Damjan, Borota, Dragan, Roessiger, Joerg, Bosela, Michal, Seben, Vladimir, Skudnik, Mitja, Adame, Patricia, Alberdi, Iciar, Canellas, Isabel, Lind, Torgny, Trubins, Renats, Thurig, Esther, Stadelmann, Golo, Ditchburn, Ben, Ross, David, Gilbert, Justin, Halsall, Lesley, Lier, Markus, and Packalen, Tuula

Published
2019

12. The European Forestry Dynamics Model: Concept, design and results of first case studies

Author

PACKALEN Tuula, SALLNAES PETTER, SIRKIA Seija, KOHONEN Kari, SALMINEN Olli, VIDAL CLAUDE, ROBERT Nicolas, COLIN Antoine, BELOUARD Thierry, SCHADAUER Klemens, BERGER Ambros, REGO Francisco, LOURO Graca, CAMIA Andrea, RÄTY MINNA, and SAN-MIGUEL-AYANZ Jesus

Abstract

The European Forestry Dynamics Model (EFDM) is a joint effort between the European Commission Joint Research Centre and partners in the EU Member States for the development of a forestry dynamics model. The model is expected to project the state of Europe’s forests given different climatic, economic and management scenarios. EFDM was designed as a flexible system to facilitate the different types of data input that are available from the diverse National Forest Inventories. The model captures different typologies such as site productivity, ownership and the probability of natural disturbances. Specifically, EFDM is able to process detailed national-level input data such as National Forest Inventories (NFI) outputs, as well as related national-level expertise in social and economic domains. In this way, the system supports effective utilization of the collaborative expertise in the parameterization of scenarios. This document is intended as a general introduction to the EFDM. Experiences gained from the EFDM test applications by five NFI teams (Austria, Finland, France, Portugal and Sweden) are also summarized in this report., JRC.H.3-Forest Resources and Climate

Published
2014

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