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594 results on '"Koch, Barbara"'

9. Tree species diversity mapping from spaceborne optical images: The effects of spectral and spatial resolution.

Author

Liu, Xiang, Frey, Julian, Munteanu, Catalina, Denter, Martin, and Koch, Barbara

Subjects
SPATIAL resolution, SPECIES diversity, OPTICAL images, CONVEX surfaces, SPECTRAL imaging, TEXTURE analysis (Image processing)
Abstract

Increasingly available spaceborne sensors provide unprecedented opportunities for large‐scale, timely and continuous tree species diversity (TSD) monitoring. However, given differences in spectral and spatial resolutions, the choice of sensor is not always straightforward. In this work, we investigated the effects of spatial and spectral resolutions for four spaceborne sensors (RapidEye, Landsat‐8, Sentinel‐2 and PlanetScope) on TSD mapping in an area of approximately 4000 km2 within the Black Forest, Germany. We employed a random forest (RF) regression model to predict Shannon–Wiener diversity based on seven types of spectral heterogeneity metrics (texture, coefficient of variation, Rao's Q, convex hull volume, spectral angle mapper, convex hull area and spectral species diversity) and a full survey dataset from 135 one‐ha sample plots. We compared the RF model's performance across sensors and spatial resolutions. Our results demonstrated that the Sentinel‐2‐based TSD model achieved the highest accuracy (mean R2: 0.477, mean root‐mean‐square error (RMSE): 0.274). The RapidEye‐based TSD model produced lower accuracy (mean R2: 0.346, mean RMSE: 0.303), but it was better than the PlanetScope‐ and Landsat‐based TSD models. The 10 m (for Sentinel‐2 and RapidEye) and 15 m (for PlanetScope) were the best spatial resolutions for predicting TSD. The NIR band was the most favourable spectral band for predicting TSD. Texture metrics and Rao's Q outperformed the other spectral heterogeneity metrics. Our results highlighted that spaceborne optical imagery (especially Sentinel‐2) can be successfully used for large‐scale TSD mapping but that the choice of sensors can significantly affect the resulting mapping accuracy in temperate montane forests. [ABSTRACT FROM AUTHOR]

Published
2024
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12. A Multimodal Lifestyle Psychosocial Survivorship Program in Young Cancer Survivors

Author

von Grundherr, Julia, primary, Elmers, Simon, additional, Koch, Barbara, additional, Hail, Lesley-Ann, additional, Mann, Julia, additional, Escherich, Gabriele, additional, Bergelt, Corinna, additional, Samland, Luisa, additional, Jensen, Wiebke, additional, Vettorazzi, Eik, additional, Stark, Maria, additional, Valentini, Luzia, additional, Baumann, Freerk T., additional, Singer, Susanne, additional, Reer, Rüdiger, additional, Beller, Ronja, additional, Calaminus, Gabriele, additional, Faber, Jörg, additional, Classen, Carl Friedrich, additional, Gebauer, Judith, additional, Hilgendorf, Inken, additional, Koehler, Michael, additional, Puzik, Alexander, additional, Salzmann, Nicole, additional, Sander, Annette, additional, Schiffmann, Lisa, additional, Sokalska-Duhme, Magdalena, additional, Schuster, Sonja, additional, Kock-Schoppenhauer, Ann-Kristin, additional, Bokemeyer, Carsten, additional, Sinn, Marianne, additional, Stein, Alexander, additional, Dwinger, Sarah, additional, and Salchow, Jannike, additional

Published
2024
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15. A regional cross-border approach to the energy transition

Author

Gavrilut, Ines, primary, Kytzia, Felix, additional, Izmailova, Kristina, additional, Najjar, Zeina, additional, Koch, Barbara, additional, Guevara-Luna, Marco Andrés, additional, Barth, Adrien, additional, Clappier, Alain, additional, Blond, Nadège, additional, Miocic, Johannes, additional, Dehler-Holland, Joris, additional, and Canaan, Bushra, additional

Published
2021
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19. Effects of an Integrative Day Care Clinic Program with a Focus on Nature Therapy in a Hospital Park Setting on Quality of Life in Oncological Patients—A Non-Randomized Controlled Study

Author

Kuballa, Lisa, primary, Kessler, Christian S., additional, Kandil, Farid I., additional, von Scheidt, Christel, additional, Meinköhn, Meline, additional, Koch, Barbara, additional, Wischnewsky, Manfred, additional, Michalsen, Andreas, additional, and Jeitler, Michael, additional

Published
2023
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21. The Value of Global Earth Observations

Author

Obersteiner, Michael, Balkovič, Juraj, Böttcher, Hannes, Bouma, Jetske A., Fritz, Steffen, Fuss, Sabina, Havlik, Peter, Heumesser, Christine, Hochrainer, Stefan, Jantke, Kerstin, Khabarov, Nikolay, Koch, Barbara, Kraxner, Florian, Kuik, Onno J., Leduc, Sylvain, Liu, Junguo, Lucht, Wolfgang, McCallum, Ian, Mechler, Reinhard, Moltchanova, Elena, Reyers, Belinda, Rydzak, Felicjan, Schill, Christian, Schleupner, Christine, Schmid, Erwin, Schneider, Uwe A., Scholes, Robert J., See, Linda, Skalský, Rastislav, Smirnov, Alexey, Szolgayova, Jana, Tarasovičová, Zuzana, Yang, Hong, Onoda, Masami, editor, and Young, Oran R, editor

Published
2017
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35. Strict rule-based automatic training data extraction using Mobile laser scanning in urban area

Author

Ma, Zhenyu, Oude Elberink, Sander, Lin, Yaping, Xu, Panpan, Xiang, Binbin, Koch, Barbara, Weinacker, Holger, Ma, Zhenyu, Oude Elberink, Sander, Lin, Yaping, Xu, Panpan, Xiang, Binbin, Koch, Barbara, and Weinacker, Holger

Abstract

To reduce the cost of manually annotating training data for supervised classifiers, we propose an automated approach to extract training data of urban objects in six classes: buildings, fences, man-made poles, vegetation, vehicles, and low objects. In this study, two segmentation algorithms are firstly implemented to generate meaningful objects from the non-ground point cloud. Then, we generated valid strict rules to label partial RANSAC (Random Sample Consensus) planes and meaningful objects as training data. The strict rules are built upon the semantic knowledge formed by the features of geometric, eigenvalue, RANSAC plane, multidimensional slice, and relative location. The accuracy of strict rule-based (SRB) training data is higher than 98.5 % for buildings, man-made poles, vegetation, and vehicles. The accuracy of low objects and fences reaches 97.10 % and 94.99 %, respectively. Finally, we compared the performance of the KPConv and PointNET++ networks trained by SRB and manually labeled training data to evaluate the effectiveness of our training data. The KPConv overall accuracy using manually labeled and (SRB) training data are 91.5 % and 86.8 % in the Paris dataset, 95.6 % and 92.0 % in the Freiburg dataset, respectively. The experiments demonstrate that automatically labeled training data can achieve similar accuracy compared to manual labels when coupled with two deep learning networks. Therefore, SRB training data extraction can effectively deal with the problem of training data scarcity and provide significant advancements in urban point cloud classification, where manual labeling of training data remains a crucial challenge.

Published
2023

48. Girls and Women in STEM

Author

Janice Koch, Barbara Polnick, Beverly Irby and Janice Koch, Barbara Polnick, Beverly Irby

Published
2017

49. Segmentation of Forest to Tree Objects

Author

Koch, Barbara, Kattenborn, Teja, Straub, Christoph, Vauhkonen, Jari, von Gadow, Klaus, Series editor, Pukkala, Timo, Series editor, Tomé, Margarida, Series editor, Maltamo, Matti, editor, Næsset, Erik, editor, and Vauhkonen, Jari, editor

Published
2014
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