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1. Multi-Modal Vision Transformers for Crop Mapping from Satellite Image Time Series

Author

Follath, Theresa, Mickisch, David, Hemmerling, Jan, Erasmi, Stefan, Schwieder, Marcel, and Demir, Begüm

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Using images acquired by different satellite sensors has shown to improve classification performance in the framework of crop mapping from satellite image time series (SITS). Existing state-of-the-art architectures use self-attention mechanisms to process the temporal dimension and convolutions for the spatial dimension of SITS. Motivated by the success of purely attention-based architectures in crop mapping from single-modal SITS, we introduce several multi-modal multi-temporal transformer-based architectures. Specifically, we investigate the effectiveness of Early Fusion, Cross Attention Fusion and Synchronized Class Token Fusion within the Temporo-Spatial Vision Transformer (TSViT). Experimental results demonstrate significant improvements over state-of-the-art architectures with both convolutional and self-attention components., Comment: 5 pages, 2 figures, 1 table. Accepted at IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2024. Our code is available at https://git.tu-berlin.de/rsim/mmtsvit

Published
2024

9. Woody semi‐natural habitats modulate the effects of field size and functional crop diversity on farmland birds

Author

Frank, Claudia, primary, Hertzog, Lionel, additional, Klimek, Sebastian, additional, Schwieder, Marcel, additional, Tetteh, Gideon Okpoti, additional, Böhner, Hannah G. S., additional, Röder, Norbert, additional, Levers, Christian, additional, Katzenberger, Jakob, additional, Kreft, Holger, additional, and Kamp, Johannes, additional

Published
2024
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10. Grassland‐use intensity maps for Switzerland based on satellite time series: Challenges and opportunities for ecological applications.

Author

Weber, Dominique, Schwieder, Marcel, Ritter, Lukas, Koch, Tiziana, Psomas, Achilleas, Huber, Nica, Ginzler, Christian, and Boch, Steffen

Subjects
GRASSLANDS, TIME series analysis, BIOINDICATORS, PLANT diversity, BIODIVERSITY conservation, LANDSAT satellites
Abstract

Land‐use intensification in grassland ecosystems (i.e. increased mowing frequency, intensified grazing) has a strong negative effect on biodiversity and ecosystem services. However, accurate information on grassland‐use intensity is difficult to acquire and restricted to the local or regional level. Recent studies have shown that mowing events can be mapped for large areas using satellite image time series. The transferability of such approaches, especially to mountain areas, has been little explored, however, and the relevance for ecological applications in biodiversity and conservation has hardly been investigated. Here, we used a rule‐based algorithm to produce annual maps for 2018–2021 of grassland‐management events, that is, mowing and/or grazing, for Switzerland using Sentinel‐2 and Landsat 8 satellite data. We assessed the detection of management events based on independent reference data, which we acquired from daily time series of publicly available webcams that are widely distributed across Switzerland. We further examined the relationships between the generated grassland‐use intensity measures and plant species richness and ecological indicator values derived from a nationwide field survey. The webcam‐based verification for 2020 and 2021 revealed that most detected management events were actual mowing/grazing events (≥78%), but that a substantial number of events were not detected (up to 57%), particularly grazing events at higher elevations. We found lower plant species richness and higher mean ecological indicator values for nutrients and mowing tolerance with more frequent management events and those starting earlier in the year. A large proportion of the variance was explained by our use‐intensity measures. Our findings therefore highlight that remotely assessed management events can characterise land‐use intensity at fine spatial and temporal resolutions across broad scales and can explain plant biodiversity patterns in grasslands. [ABSTRACT FROM AUTHOR]

Published
2024
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16. High spatial resolution mapping identifies habitat characteristics of the invasive vine Antigonon leptopus on St. Eustatius (Lesser Antilles)

Author

Haber, Elizabeth A., Santos, Maria J., Leitão, Pedro J., Schwieder, Marcel, Ketner, Pieter, Ernst, Joris, Rietkerk, Max, Wassen, Martin J., Eppinga, Maarten B., Spatial Ecology and Global Change, and Environmental Sciences

Subjects
Caribbean, Vine, landscape ecology, Support Vector Machine, Ecology, biology, Evolution, Antigonon leptopus, biology.organism_classification, Invasive species, invasive species, Plant ecology, remote sensing, Geography, Behavior and Systematics, Habitat, Remote sensing (archaeology), High spatial resolution, Landscape ecology, plant ecology, Ecology, Evolution, Behavior and Systematics
Abstract

On the Caribbean island of St. Eustatius, Coralita (Antigonon leptopus) is an aggressive invasive vine posing major biodiversity conservation concerns. The generation of distribution maps can address these conservation concerns by helping to elucidate the drivers of invasion. We test the use of support vector machines to map the distribution of Coralita on St. Eustatius at high spatial resolution and use this map to identify potential landscape and geomorphological factors associated with Coralita presence. This latter step was performed by comparing the actual distribution of Coralita patches to a random distribution of patches. To train the support vector machine algorithm, we used three vegetation indices and seven texture metrics derived from a 2014 WorldView-2 image. The resulting map shows that Coralita covered 3.18% of the island in 2014, corresponding to an area of 64 ha. The mapped distribution was highly accurate, with 93.2% overall accuracy (Coralita class producer's accuracy: 76.4%, user's accuracy: 86.2%). Using this classification map, we found that Coralita is not randomly distributed across the landscape, occurring significantly closer to roads and drainage channels, in areas with higher accumulated moisture, and on flatter slopes. Coralita was found more often than expected in grasslands, disturbed forest, and urban areas but was relatively rare in natural forest. These results highlight the ability of high spatial resolution data from sensors such as WorldView-2 to produce accurate invasive species, providing valuable information for predicting current and future spread risks and for early detection and removal plans. Abstract in Dutch is available with online material.

Published
2021

17. Landsat time series reveal simultaneous expansion and intensification of irrigated dry season cropping in Southeastern Turkey

Author

Rufin, Philippe, Müller, Daniel, Schwieder, Marcel, Pflugmacher, Dirk, Hostert, Patrick, Humboldt-Universität zu Berlin - Geography Department, and UCL - SST/ELI/ELIC - Earth & Climate

Subjects
Planning and Development, 551 Geologie, Hydrologie, Meteorologie, Monitoring, Policy and Law, Geography, cropping intensity, irrigation, Management, fallow frequency, water resource management, ddc:551, ddc:330, crop water requirements, Google Earth Engine, Earth-Surface Processes
Abstract

Long-term monitoring of the extent and intensity of irrigation systems is needed to track crop water consumption and to adapt land use to a changing climate. We mapped the expansion and changes in the intensity of irrigated dry season cropping in Turkey´s Southeastern Anatolia Project annually from 1990 to 2018 using Landsat time series. Irrigated dry season cropping covered 5,779 km² (± 479 km²) in 2018, which represents an increase of 617% over the study period. Dry season cropping was practiced on average every second year, but spatial variability was pronounced. Increases in dry season cropping frequency were observed on 40% of the studied croplands. The presented maps enable the identification of land use intensity hotspots at 30 m spatial resolution, and can thus aid in assessments of water consumption and environmental degradation. All maps are openly available for further use at https://doi.org/10.5281/zenodo.4287661.

Published
2021

18. Quantification of Ownership Concentration from Cadastral Records of Agricultural Land in Märkisch-Oderland

Author

Müller, Daniel, Rufin, Philippe, and Schwieder, Marcel

Subjects
Land ownership, agricultural structure, land concentration, land use, Land Economics/Use
Abstract

Concentration of ownership of agricultural land in fewer hands has often been highlighted as a side effect of the increasing capital resources that are allocated to the agricultural sector. One key concern is that, as more land concentrates in fewer hands, the few large actors can exert power on land markets by dominating prices through regulating supply and demand of land. Unfortunately, empirical evidence on the degree of market power on land markets remains scarce, mainly due to a lack of ownership data. We shed light on the concentration of ownership in agricultural land by analyzing the complete cadastral records of agricultural land at one point in time and for one district in the federal state of Brandenburg. We present the workflow to process the cadastral data for subsequent analysis in GIS and statistical software packages. For our study area, we derive relative and absolute concentration measures for the ownership in agricultural land. Our results suggest high relative concentration on the district level with a Gini coefficient of 0.85. Within the district, we see varying degrees of land concentration, albeit spatial clusters of high and low concentration. Our methodological approach holds great promise because it can be expanded to larger areas and different time periods. However, the cadastral data does not allow to infer on the underlying corporate structures, such as those of large investors who may own several agricultural companies. Such corporative structures may, through their local subsidiaries that could be spatially clustered, exert market power to the detriment of local land supply markets. Additional data and analysis using, for example, registers of company registers, need to be combined with the cadastral data to reveal such structures.

Published
2021
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19. High spatial resolution mapping identifies habitat characteristics of the invasive vine Antigonon leptopus on St. Eustatius (Lesser Antilles)

Author

Spatial Ecology and Global Change, Environmental Sciences, Haber, Elizabeth A., Santos, Maria J., Leitão, Pedro J., Schwieder, Marcel, Ketner, Pieter, Ernst, Joris, Rietkerk, Max, Wassen, Martin J., Eppinga, Maarten B., Spatial Ecology and Global Change, Environmental Sciences, Haber, Elizabeth A., Santos, Maria J., Leitão, Pedro J., Schwieder, Marcel, Ketner, Pieter, Ernst, Joris, Rietkerk, Max, Wassen, Martin J., and Eppinga, Maarten B.

Published
2021

20. Quantification of Ownership Concentration from Cadastral Records of Agricultural Land in Märkisch-Oderland

Author

Humboldt-Universität zu Berlin - Geography Department, Müller, Daniel, Rufin, Marcel Philippe, Schwieder, Marcel, Humboldt-Universität zu Berlin - Geography Department, Müller, Daniel, Rufin, Marcel Philippe, and Schwieder, Marcel

Abstract

Concentration of ownership of agricultural land in fewer hands has often been highlighted as a side effect of the increasing capital resources that are allocated to the agricultural sector. One key concern is that, as more land concentrates in fewer hands, the few large actors can exert power on land markets by dominating prices through regulating supply and demand of land. Unfortunately, empirical evidence on the degree of market power on land markets remains scarce, mainly due to a lack of ownership data. We shed light on the concentration of ownership in agricultural land by analyzing the complete cadastral records of agricultural land at one point in time and for one district in the federal state of Brandenburg. We present the workflow to process the cadastral data for subsequent analysis in GIS and statistical software packages. For our study area, we derive relative and absolute concentration measures for the ownership in agricultural land. Our results suggest high relative concentration on the district level with a Gini coefficient of 0.85. Within the district, we see varying degrees of land concentration, albeit spatial clusters of high and low concentration. Our methodological approach holds great promise because it can be expanded to larger areas and different time periods. However, the cadastral data does not allow to infer on the underlying corporate structures, such as those of large investors who may own several agricultural companies. Such corporative structures may, through their local subsidiaries that could be spatially clustered, exert market power to the detriment of local land supply markets. Additional data and analysis using, for example, registers of company registers, need to be combined with the cadastral data to reveal such structures.

Published
2021

21. Landsat time series reveal simultaneous expansion and intensification of irrigated dry season cropping in Southeastern Turkey

Author

Humboldt-Universität zu Berlin - Geography Department, UCL - SST/ELI/ELIC - Earth & Climate, Rufin, Philippe, Müller, Daniel, Schwieder, Marcel, Pflugmacher, Dirk, Hostert, Patrick, Humboldt-Universität zu Berlin - Geography Department, UCL - SST/ELI/ELIC - Earth & Climate, Rufin, Philippe, Müller, Daniel, Schwieder, Marcel, Pflugmacher, Dirk, and Hostert, Patrick

Published
2021

22. Combining environmental and Landsat analysis ready data for vegetation mapping: a case study in the Brazilian savanna biome

Author

Humboldt-Universität zu Berlin - Geography Department, Bendini, Hugo N., Fonseca, Leila M. G., Schwieder, Marcel, Rufin, Marcel Philippe, Korting, Thales S., Koumrouyan, A., Hostert, Patrick, Humboldt-Universität zu Berlin - Geography Department, Bendini, Hugo N., Fonseca, Leila M. G., Schwieder, Marcel, Rufin, Marcel Philippe, Korting, Thales S., Koumrouyan, A., and Hostert, Patrick

Published
2020

23. Applying A Phenological Object-Based Image Analysis (Phenobia) for Agricultural Land Classification: A Study Case in the Brazilian Cerrado

Author

Humboldt-Universität zu Berlin - Geography Department, Bendini, Hugo N., Fonseca, Leila M. G., Soares, Anderson R., Rufin, Marcel Philippe, Schwieder, Marcel, Rodrigues, Marcos A., Maretto, Raian V., Korting, Thales S., Leitao, Pedro J., Sanches, Ieda D. A., Hostert, Patrick, Humboldt-Universität zu Berlin - Geography Department, Bendini, Hugo N., Fonseca, Leila M. G., Soares, Anderson R., Rufin, Marcel Philippe, Schwieder, Marcel, Rodrigues, Marcos A., Maretto, Raian V., Korting, Thales S., Leitao, Pedro J., Sanches, Ieda D. A., and Hostert, Patrick

Abstract

Mapping agriculture with high accuracy is important to generate reliable information about crop production. Pixel-based methods still present problems with noise and usually require post-processing approaches to reach satisfactory results. Object-based Image Analysis (OBIA) enable the detection of homogeneous objects in remote sensing images based on spectral similarity. However, traditional OBIA does not consider the multi-temporal characteristics of land cover or land use, such as agriculture. The objective of this study is to evaluate a phenological object-based approach with dense Landsat image time series for mapping agriculture in different level of detail in the Brazilian Cerrado. We derived pixel-wise EVI fitted time series with 8-day temporal resolution and applied multi-resolution segmentation using all image bands to incorporate the influence of space and time. Then we generated phenological metrics and applied OBIA of agricultural lands in Brazil using a hierarchical classification scheme. The overall accuracies for each hierarchical level were around 90%, and the spatial consistency of the generated maps is promising.

Published
2020

24. Unsupervised Parameterization for Optimal Segmentation of Agricultural Parcels from Satellite Images in Different Agricultural Landscapes

Author

Tetteh, Gideon Okpoti, Gocht, Alexander, Schwieder, Marcel, Erasmi, Stefan, Conrad, Christopher, Tetteh, Gideon Okpoti, Gocht, Alexander, Schwieder, Marcel, Erasmi, Stefan, and Conrad, Christopher

Abstract

Image segmentation is a cost-effective way to obtain information about the sizes and structural composition of agricultural parcels in an area. To accurately obtain such information, the parameters of the segmentation algorithm ought to be optimized using supervised or unsupervised methods. The difficulty in obtaining reference data makes unsupervised methods indispensable. In this study, we evaluated an existing unsupervised evaluation metric that minimizes a global score (GS), which is computed by summing up the intra-segment uniformity and inter-segment dissimilarity within a segmentation output. We modified this metric and proposed a new metric that uses absolute difference to compute the GS. We compared this proposed metric with the existing metric in two optimization approaches based on the Multiresolution Segmentation (MRS) algorithm to optimally delineate agricultural parcels from Sentinel-2 images in Lower Saxony, Germany. The first approach searches for optimal scale while keeping shape and compactness constant, while the second approach uses Bayesian optimization to optimize the three main parameters of the MRS algorithm. Based on a reference data of agricultural parcels, the optimal segmentation result of each optimization approach was evaluated by calculating the quality rate, over-segmentation, and under-segmentation. For both approaches, our proposed metric outperformed the existing metric in different agricultural landscapes. The proposed metric identified optimal segmentations that were less under-segmented compared to the existing metric. A comparison of the optimal segmentation results obtained in this study to existing benchmark results generated via supervised optimization showed that the unsupervised Bayesian optimization approach based on our proposed metric can potentially be used as an alternative to supervised optimization, particularly in geographic regions where reference data is unavailable or an automated evaluation system is sought., Peer Reviewed

Published
2020

27. Applying A Phenological Object-Based Image Analysis (Phenobia) for Agricultural Land Classification: A Study Case in the Brazilian Cerrado

Author

Bendini, Hugo N., primary, Fonseca, Leila M. G., additional, Soares, Anderson R., additional, Rufin, Philippe, additional, Schwieder, Marcel, additional, Rodrigues, Marcos A., additional, Maretto, Raian V., additional, Korting, Thales S., additional, Leitao, Pedro J., additional, Sanches, Ieda D. A., additional, and Hostert, Patrick, additional

Published
2020
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30. Detailed agricultural land classification in the Brazilian cerrado based on phenological information from dense satellite image time series

Author

Humboldt-Universität zu Berlin - Geography Department, UCL - SST/ELI/ELIC - Earth & Climate, do Nascimento Bendini, Hugo, Garcia Fonseca, Leila Maria, Schwieder, Marcel, Sehn Körting, Thales, Rufin, Philippe, Del Arco Sanches, Ieda, Leitão, Pedro J., Hostert, Patrick, Humboldt-Universität zu Berlin - Geography Department, UCL - SST/ELI/ELIC - Earth & Climate, do Nascimento Bendini, Hugo, Garcia Fonseca, Leila Maria, Schwieder, Marcel, Sehn Körting, Thales, Rufin, Philippe, Del Arco Sanches, Ieda, Leitão, Pedro J., and Hostert, Patrick

Published
2019

31. Comparing Phenometrics Extracted From Dense Landsat-Like Image Time Series for Crop Classification

Author

Humboldt-Universität zu Berlin - Geography Department, Nascimento Bendini, Hugo do, Maria Garcia Fonseca, Leila, Schwieder, Marcel, Korting, Thales Sehn, Rufin, Marcel Philippe, Arco Sanches, Ieda Del, Leitao, Pedro J., Hostert, Patrick, Humboldt-Universität zu Berlin - Geography Department, Nascimento Bendini, Hugo do, Maria Garcia Fonseca, Leila, Schwieder, Marcel, Korting, Thales Sehn, Rufin, Marcel Philippe, Arco Sanches, Ieda Del, Leitao, Pedro J., and Hostert, Patrick

Published
2019

32. Land surface phenological archetypes of the Cerrado

Author

Humboldt-Universität zu Berlin - Geography Department, Schwieder, Marcel, Leitão, Pedro .J., Rufin, Marcel Philippe, Rabe, Andreas, Levers, Christian, Bendini, Hugo, Ferreira, Laerte G., Bustamante, Mercedes .M., Hostert, Patrick, Humboldt-Universität zu Berlin - Geography Department, Schwieder, Marcel, Leitão, Pedro .J., Rufin, Marcel Philippe, Rabe, Andreas, Levers, Christian, Bendini, Hugo, Ferreira, Laerte G., Bustamante, Mercedes .M., and Hostert, Patrick

Published
2019

33. Make EU trade with Brazil sustainable

Author

Kehoe, Laura, Reis, Tiago, Virah-Sawmy, Malika, Balmford, Andrew, Kuemmerle, Tobias, Knohl, Alexander, Antonelli, Alexandre, Hochkirch, Axel, Vira, Bhaskar, Massa, Bruno, Peres, Carlos A., Ammer, Christian, Goerg, Christoph, Schneider, Christoph, Curtis, David, de la Pena, Eduardo, Tello, Enric, Sperfeld, Erik, Corbera, Esteve, Morelli, Federico, Valladares, Fernando, Peterson, Garry, Hide, Geoff, Mace, Georgina, Kallis, Giorgos, Olsson, Gunilla Almered, Brumelis, Guntis, Alexanderson, Helena, Haberl, Helmut, Nuissl, Henning, Kreft, Holger, Ghazoul, Jaboury, Piotrowski, Jan A., Macdiarmid, Jennie, Newig, Jens, Fischer, Joern, Altringham, John, Gledhill, John, Nielsen, Jonas O., Mueller, Joerg, Palmeirim, Jorge, Barlow, Jos, Alonso, Juan C., Presa Asencio, Juan Jose, Steinberger, Julia K., Jones, Julia Patricia Gordon, Cabral, Juliano Sarmento, Dengler, Juergen, Stibral, Karel, Erb, Karlheinz, Rothhaupt, Karl-Otto, Wiegand, Kerstin, Cassar, Louis F., Lens, Luc, Rosalino, Luis Miguel, Wassen, M. J., Stenseke, Marie, Fischer-Kowalski, Marina, Diaz, Mario, Rounsevell, Mark, van Kleunen, Mark, Junginger, Martin, Kaltenpoth, Martin, Zobel, Martin, Weigend, Maximilian, Partel, Meelis, Schilthuizen, Menno, Bastos Araujo, Miguel, Haklay, Muki, Eisenhauer, Nico, Selva, Nuria, Mertz, Ole, Meyfroidt, Patrick, Borges, Paulo A. V., Kovar, Pavel, Smith, Pete, Verburg, Peter, Pysek, Petr, Seppelt, Ralf, Valentini, Riccardo, Whittaker, Robert J., Henrique Faria, Sergio, Ulgiati, Sergio, Loetters, Stefan, Bjorck, Svante, Larson, Sven Ake, Tscharntke, Teja, Domingos, Tiago, Krueger, Tobias, Pascual, Unai, Olsson, Urban, Kati, Vassiliki, Winiwarter, Verena, Reyes-Garcia, Victoria, Vajda, Vivi, Sutherland, William J., de Waroux, Yann le Polain, Buckley, Yvonne, Rammig, Anja, Kasimir, Asa, Crona, Beatrice, Sindicic, Magda, Persson, Martin, Lapka, Miloslav, Di Gregorio, Monica, Hahn, Thomas, Boonstra, Wiebren, Lipsky, Zdenek, Zucaro, A., Roeder, Achim, Lopez Baucells, Adria, Danet, Alain, Franco, Aldina, Nieto Roman, Alejandra, Lehikoinen, Aleksi, Collalti, Alessio, Keller, Alexander, Strugariu, Alexandru, Perrigo, Allison, Fernandez-Llamazares, Alvaro, Salaseviciene, Alvija, Hinsley, Amy, Santos, Ana M. C., Novoa, Ana, Rodrigues, Ana, Mascarenhas, Andre, Martins, Andrea Damacena, Holzschuh, Andrea, Meseguer, Andrea S., Hadjichambis, Andreas, Mayer, Andreas, Hacket-Pain, Andrew, Ringsmuth, Andrew, de Frutos, Angel, Stein, Anke, Heikkinen, Anna, Smith, Annabel, Bjoersne, Anna-Karin, Bagneres, Anne-Genevieve, Machordom, Annie, Kristin, Anton, Ghoddousi, Arash, Staal, Arie, Martin, Arnaud, Taylor, Astrid, Borrell, Asuncion, Marescaux, Audrey, Torres, Aurora, Helm, Aveliina, Bauer, Barbara, Smetschka, Barbara, Rodriguez-Labajos, Beatriz, Peco, Begona, Gambin, Belinda, Celine, Bellard, Phalan, Ben, Cotta, Benedetta, Rugani, Benedetto, Jarcuska, Benjamin, Leroy, Boris, Nikolov, Boris Petrov, Milchev, Boyan Petrov, Brown, Calum, Ritter, Camila Duarte, Gomes, Carmen Bessa, Meyer, Carsten, Munteanu, Catalina, Penone, Caterina, Friis, Cecilie, Teplitsky, Celine, Roemer, Charlotte, Orland, Chloe, Voigt, Christian C., Levers, Christian, Zang, Christian, Bacon, Christine D., Meyer, Christoph, Wordley, Claire, Grilo, Clara, Cattaneo, Claudio, Battisti, Corrado, Banks-Leite, Cristina, Zurell, Damaris, Challender, Dan, Mueller, Daniel, Matenaar, Daniela, Silvestro, Daniele, McKay, David Armstrong, Buckley, David, Frantz, David, Gremillet, David, Mateos, David Moreno, Sanchez-Fernandez, David, Vieites, David, Ascoli, Davide, Arlt, Debora, Louis, Deharveng, Zemp, Delphine Clara, Strubbe, Diederik, Gil, Diego, Llusia, Diego, Bennett, Dominic J., Chobanov, Dragan Petrov, Aguilera, Eduardo, Oliveira, Eduardo, Pynegar, Edwin L., Granda, Elena, Grieco, Elisa, Conrad, Elisabeth, Revilla, Eloy, Lindkvist, Emilie, Caprio, Enrico, zu Ermgassen, Erasmus, Berenguer, Erika, Ochu, Erinma, Polaina, Ester, Nuernberger, Fabian, Esculier, Fabien, de Castro, Fabio, Albanito, Fabrizio, Langerwisch, Fanny, Batsleer, Femke, Ascensao, Fernando, Moyano, Fernando Esteban, Sayol, Ferran, Buzzetti, Filippo Maria, Eiro, Flavio, Volaire, Florence, Gollnow, Florian, Menzel, Florian, Pilo, Francesca, Moreira, Francisco, Briens, Francois, Essl, Franz, Vlahos, George, Billen, Gilles, Vacchiano, Giorgio, Wong, Grace, Gruychev, Gradimir Valentinov, Fandos, Guillermo, Petter, Gunnar, Sinare, Hanna, Mumby, Hannah S., Cottyn, Hanne, Seebens, Hanno, Bjorklund, Heidi, Schroeder, Heike, Lopez Hernandez, Heriberto D., Rebelo, Hugo, Chenet, Hugues, De la Riva, Ignacio, Torre, Ignasi, Aalders, Inge, Grass, Ingo, Chuine, Isabelle, Goepel, Jan, Wieringa, Jan J., Engler, Jan O., Pergl, Jan, Schnitzler, Jan, Vavra, Jan, Medvedovic, Jasna, Cabello, Javier, Martin, Jean-Louis, Mutke, Jens, Lewis, Jerome, da Silva, Jessica Fonseca, Marull, Joan, Carvalho, Joana, Carnicer, Jofre, Enqvist, Johan, Simaika, John P., Noguera, Jose C., Blanco Moreno, Jose M., Bruna, Josef, Garnier, Josette, Fargallo, Juan A., Rocha, Juan Carlos, Carrillo, Juan D., Infante-Amate, Juan, Traba Diaz, Juan, Schleicher, Judith, Simon, Judy, Noe, Julia Le, Gerlach, Justin, Eriksson, K. Martin, Prince, Karine, Ostapowicz, Katarzyna, Stajerova, Katerina, Farrell, Katharine N., Snell, Katherine, Yates, Katherine, Fleischer, Katrin, Darras, Kevin, Schumacher, Kim, Orach, Kirill, Thonicke, Kirsten, Riede, Klaus, Heller, Klaus-Gerhard, Wang-Erlandsson, Lan, Pereira, Laura, Riggi, Laura, Florez, Laura V., Emperaire, Laure, Durieux, Laurent, Tatin, Laurent, Rozylowicz, Laurentiu, Latella, Leonardo, Andresen, Louise C., Cahen-Fourot, Louison, de Agua, Luis Borda, Boto, Luis, Lassaletta, Luis, Amo, Luisa, Sekerka, Lukas, Morales, Manuel B., Macia, Manuel J., Suarez, Manuela Gonzalez, Cabeza, Mar, Londo, Marc, Pollet, Marc, Schwieder, Marcel, Peters, Marcell K., D'Amico, Marcello, Casazza, Marco, Florencio, Margarita, Felipe-Lucia, Maria, Gebara, Maria Fernanda, Johansson, Maria, Garcia, Maria Mancilla, Piquer-Rodriguez, Maria, Tengo, Maria, Elias, Marianne, Leve, Marine, Conde, Marta, Winter, Marten, Koster, Martijn, Mayer, Martin, Salek, Martin, Schlerf, Martin, Sullivan, Martin, Baumann, Matthias, Pichler, Melanie, Marselle, Melissa, Oddie, Melissa, Razanajatovo, Mialy, Borregaard, Michael Krabbe, Theurl, Michaela C., Hernandez, Miguel, Krofel, Miha, Kechev, Mihail Ognianov, Clark, Mike, Rands, Mike, Antal, Miklos, Pucetaite, Milda, Islar, Mine, Truong, Minh-Xuan A., Vighi, Morgana, Johanisova, Nadia, Prat, Narcis, Escobar, Neus, Deguines, Nicolas, Rust, Niki, Zafra-Calvo, Noelia, Maurel, Noelie, Wagner, Norman, Fitton, Nuala, Ostermann, Ole, Panferov, Oleg, Ange, Olivia, Canals, Oriol, Englund, Oskar, De Smedt, Pallieter, Petridis, Panos, Heikkurinen, Pasi, Weigelt, Patrick, Henriksson, Patrik J. G., de Castro, Paula Drummond, Matos-Maravi, Pavel, Duran, Paz, Aragon, Pedro, Cardoso, Pedro, Leitao, Pedro J., Hosner, Peter A., Biedermann, Peter, Keil, Petr, Petrik, Petr, Martin, Philip, Bocquillon, Pierre, Renaud, Pierre-Cyril, Addison, Prue, Antwis, Rachael, Carmenta, Rachel, Barrientos, Rafael, Smith, Rebecca, Rocha, Ricardo, Fuchs, Richard, Felix, Rob, Kanka, Robert, Aguilee, Robin, Padro Caminal, Roc, Libbrecht, Romain, Lorrilliere, Romain, van der Ent, Ruud J., Henders, Sabine, Pueyo, Salvador, Roturier, Samuel, Jacobs, Sander, Lavorel, Sandra, Leonhardt, Sara Diana, Fraixedas, Sara, Villen-Perez, Sara, Cornell, Sarah, Redlich, Sarah, De Smedt, Sebastian, van der Linden, Sebastian, Perez-Ortega, Sergio, Petrovan, Silviu, Cesarz, Simone, Sjoberg, Sissel, Caillon, Sophie, Schindler, Stefan, Trogisch, Stefan, Taiti, Stefano, Oppel, Steffen, Lutter, Stephan, Garnett, Tara, Guedes, Thais, Wanger, Thomas Cherico, Kastner, Thomas, Worthington, Thomas, Daw, Tim, Schmoll, Tim, McPhearson, Timon, Engl, Tobias, Rutting, Tobias, Vaclavik, Tomas, Jucker, Tommaso, Robillard, Tony, Krause, Torsten, Ljubomirov, Toshko, Aavik, Tsipe, Richardson, Vanessa A., Masterson, Vanessa Anne, Seufert, Verena, Cathy, Vet Gibault, Colino Rabanal, Victor, Montade, Vincent, Thieu, Vincent, Sober, Virve, Morin, Xavier, Mehrabi, Zia, Gonzalez, Adriana Trompetero, Sanz-Cobena, Alberto, Christie, Alec Philip, Romero-Munoz, Alfredo, Dauriach, Alice, Queiroz, Allan Souza, Golland, Ami, Evans, Amy Louise, Cordero, Ana Maria Araujo, Dara, Andrey, Rilovic, Andro, Pedersen, Anna Frohn, Csergo, Anna Maria, Lewerentz, Anne, Monserand, Antoine, Valdecasas, Antonio G., Doherty, Anya, Semper-Pascual, Asuncion, Bleyhl, Benjamin, Rutschmann, Benjamin, Bongalov, Boris, Hankerson, Brett, Heylen, Brigitte, Alonso-Alvarez, Carlos, Comandulli, Carolina, Frossard, Carolina M., Mckeon, Caroline, Godde, Cecile, Palm, Celinda, Singh, Chandrakant, Sieger, Charlotte Sophie, Ohrling, Christian, Paitan, Claudia Parra, Cooper, Conor, Edler, Daniel, Roessler, Daniela C., Kessner-Beierlein, Daniela, Garcia del Amo, David, Lopez Bosch, David, Gueldner, Dino, Noll, Domink, Motivans, Elena, Canteri, Elisabetta, Garnett, Emma, Malecore, Eva, Brambach, Fabian, Ruedenauer, Fabian, Yin, Fang, Hurtado, Fernando, Mempel, Finn, de Freitas, Flavio Luiz Mazzaro, Pendrill, Florence, Leijten, Floris, Somma, Francesca, Schug, Franz, De Knijf, Geert, Peterson, Gustaf, Pe'er, Guy, Booth, Hollie, Rhee, Howon, Staude, Ingmar, Gherghel, Iulian, Vila Traver, Jaime, Kerner, Janika, Hinton, Jennifer, Hortal, Joaquin, Persson, Joel, Uddling, Johan, Coenen, Johanna, Geldmann, Jonas, Geschke, Jonas, Juergensen, Jonathan, Lobo, Jorge M., Skejo, Josip, Heinen, Julia Helena, Schuenzel, Julia, Daniel-Ferreira, Juliana, Christophe Piquet, Julien, Murtough, Katie L., Prevel, Leonie, Hissa, Leticia B. V., af Segerstad, Louise Hard, Willemse, Luc, Benavides, Lucia, Sovova, Lucie, Figueiredo, Ludmilla, Leidinger, Ludwig, Piemontese, Luigi, da Fonte, Luis Fernando Marin, Moreta, Lys Sanz, Bhan, Manan, Toledo-Hernandez, Manuel, Engert, Manuela, Davoli, Marco, Mas Navarro, Maria, Voigt, Maria, Zirion, Maria, Wandl, Marie-Theres, Kipson, Marina, Johnson, Mark D., Lukic, Marko, Goula, Marta, Jung, Martin, Nunes, Matheus Henrique, Alvarez, Matheus Rodriguez, van den Burg, Matthijs P., Guerrero, Mayra Daniela Pena, Greenfield, Michael, Lobmann, Michael, Nygren, Michelle, Guth, Miriam Karen, Koh, Niak, Stanek, Nicola, Roux, Nicolas, Karagouni, Niki, Tiralla, Nina, Mairota, Paola, Savaget, Paulo, von Doehren, Peer, Benyei, Petra, Lena, Philippe, Rufin, Philippe, Janke, Rebekka, Santagata, Remo, Motta, Renzo, Battiston, Roberto, Oyanedel, Rodrigo, Bernardo-Madrid, Ruben, Vasconcelos, Sasha, Henriques, Sergio, Bager, Simon L., Qin, Siyu, Ivkovic, Slobodan, Cooke, Sophia, Ernst, Stefan, Schmelzer, Stefan, da Silva, Sven, Faberova, Tamara, Enseroth, Tanja, De Marzo, Teresa, Pienkowski, Thomas, Engel, Thore, Boehnert, Tim, Swinfield, Tom, Kurdikova, Vendula, Chvatalova, Veronika, Lopez-Marquez, Violeta, Arlidge, William, Zhang, Zhijie, Kehoe, Laura, Reis, Tiago, Virah-Sawmy, Malika, Balmford, Andrew, Kuemmerle, Tobias, Knohl, Alexander, Antonelli, Alexandre, Hochkirch, Axel, Vira, Bhaskar, Massa, Bruno, Peres, Carlos A., Ammer, Christian, Goerg, Christoph, Schneider, Christoph, Curtis, David, de la Pena, Eduardo, Tello, Enric, Sperfeld, Erik, Corbera, Esteve, Morelli, Federico, Valladares, Fernando, Peterson, Garry, Hide, Geoff, Mace, Georgina, Kallis, Giorgos, Olsson, Gunilla Almered, Brumelis, Guntis, Alexanderson, Helena, Haberl, Helmut, Nuissl, Henning, Kreft, Holger, Ghazoul, Jaboury, Piotrowski, Jan A., Macdiarmid, Jennie, Newig, Jens, Fischer, Joern, Altringham, John, Gledhill, John, Nielsen, Jonas O., Mueller, Joerg, Palmeirim, Jorge, Barlow, Jos, Alonso, Juan C., Presa Asencio, Juan Jose, Steinberger, Julia K., Jones, Julia Patricia Gordon, Cabral, Juliano Sarmento, Dengler, Juergen, Stibral, Karel, Erb, Karlheinz, Rothhaupt, Karl-Otto, Wiegand, Kerstin, Cassar, Louis F., Lens, Luc, Rosalino, Luis Miguel, Wassen, M. J., Stenseke, Marie, Fischer-Kowalski, Marina, Diaz, Mario, Rounsevell, Mark, van Kleunen, Mark, Junginger, Martin, Kaltenpoth, Martin, Zobel, Martin, Weigend, Maximilian, Partel, Meelis, Schilthuizen, Menno, Bastos Araujo, Miguel, Haklay, Muki, Eisenhauer, Nico, Selva, Nuria, Mertz, Ole, Meyfroidt, Patrick, Borges, Paulo A. V., Kovar, Pavel, Smith, Pete, Verburg, Peter, Pysek, Petr, Seppelt, Ralf, Valentini, Riccardo, Whittaker, Robert J., Henrique Faria, Sergio, Ulgiati, Sergio, Loetters, Stefan, Bjorck, Svante, Larson, Sven Ake, Tscharntke, Teja, Domingos, Tiago, Krueger, Tobias, Pascual, Unai, Olsson, Urban, Kati, Vassiliki, Winiwarter, Verena, Reyes-Garcia, Victoria, Vajda, Vivi, Sutherland, William J., de Waroux, Yann le Polain, Buckley, Yvonne, Rammig, Anja, Kasimir, Asa, Crona, Beatrice, Sindicic, Magda, Persson, Martin, Lapka, Miloslav, Di Gregorio, Monica, Hahn, Thomas, Boonstra, Wiebren, Lipsky, Zdenek, Zucaro, A., Roeder, Achim, Lopez Baucells, Adria, Danet, Alain, Franco, Aldina, Nieto Roman, Alejandra, Lehikoinen, Aleksi, Collalti, Alessio, Keller, Alexander, Strugariu, Alexandru, Perrigo, Allison, Fernandez-Llamazares, Alvaro, Salaseviciene, Alvija, Hinsley, Amy, Santos, Ana M. C., Novoa, Ana, Rodrigues, Ana, Mascarenhas, Andre, Martins, Andrea Damacena, Holzschuh, Andrea, Meseguer, Andrea S., Hadjichambis, Andreas, Mayer, Andreas, Hacket-Pain, Andrew, Ringsmuth, Andrew, de Frutos, Angel, Stein, Anke, Heikkinen, Anna, Smith, Annabel, Bjoersne, Anna-Karin, Bagneres, Anne-Genevieve, Machordom, Annie, Kristin, Anton, Ghoddousi, Arash, Staal, Arie, Martin, Arnaud, Taylor, Astrid, Borrell, Asuncion, Marescaux, Audrey, Torres, Aurora, Helm, Aveliina, Bauer, Barbara, Smetschka, Barbara, Rodriguez-Labajos, Beatriz, Peco, Begona, Gambin, Belinda, Celine, Bellard, Phalan, Ben, Cotta, Benedetta, Rugani, Benedetto, Jarcuska, Benjamin, Leroy, Boris, Nikolov, Boris Petrov, Milchev, Boyan Petrov, Brown, Calum, Ritter, Camila Duarte, Gomes, Carmen Bessa, Meyer, Carsten, Munteanu, Catalina, Penone, Caterina, Friis, Cecilie, Teplitsky, Celine, Roemer, Charlotte, Orland, Chloe, Voigt, Christian C., Levers, Christian, Zang, Christian, Bacon, Christine D., Meyer, Christoph, Wordley, Claire, Grilo, Clara, Cattaneo, Claudio, Battisti, Corrado, Banks-Leite, Cristina, Zurell, Damaris, Challender, Dan, Mueller, Daniel, Matenaar, Daniela, Silvestro, Daniele, McKay, David Armstrong, Buckley, David, Frantz, David, Gremillet, David, Mateos, David Moreno, Sanchez-Fernandez, David, Vieites, David, Ascoli, Davide, Arlt, Debora, Louis, Deharveng, Zemp, Delphine Clara, Strubbe, Diederik, Gil, Diego, Llusia, Diego, Bennett, Dominic J., Chobanov, Dragan Petrov, Aguilera, Eduardo, Oliveira, Eduardo, Pynegar, Edwin L., Granda, Elena, Grieco, Elisa, Conrad, Elisabeth, Revilla, Eloy, Lindkvist, Emilie, Caprio, Enrico, zu Ermgassen, Erasmus, Berenguer, Erika, Ochu, Erinma, Polaina, Ester, Nuernberger, Fabian, Esculier, Fabien, de Castro, Fabio, Albanito, Fabrizio, Langerwisch, Fanny, Batsleer, Femke, Ascensao, Fernando, Moyano, Fernando Esteban, Sayol, Ferran, Buzzetti, Filippo Maria, Eiro, Flavio, Volaire, Florence, Gollnow, Florian, Menzel, Florian, Pilo, Francesca, Moreira, Francisco, Briens, Francois, Essl, Franz, Vlahos, George, Billen, Gilles, Vacchiano, Giorgio, Wong, Grace, Gruychev, Gradimir Valentinov, Fandos, Guillermo, Petter, Gunnar, Sinare, Hanna, Mumby, Hannah S., Cottyn, Hanne, Seebens, Hanno, Bjorklund, Heidi, Schroeder, Heike, Lopez Hernandez, Heriberto D., Rebelo, Hugo, Chenet, Hugues, De la Riva, Ignacio, Torre, Ignasi, Aalders, Inge, Grass, Ingo, Chuine, Isabelle, Goepel, Jan, Wieringa, Jan J., Engler, Jan O., Pergl, Jan, Schnitzler, Jan, Vavra, Jan, Medvedovic, Jasna, Cabello, Javier, Martin, Jean-Louis, Mutke, Jens, Lewis, Jerome, da Silva, Jessica Fonseca, Marull, Joan, Carvalho, Joana, Carnicer, Jofre, Enqvist, Johan, Simaika, John P., Noguera, Jose C., Blanco Moreno, Jose M., Bruna, Josef, Garnier, Josette, Fargallo, Juan A., Rocha, Juan Carlos, Carrillo, Juan D., Infante-Amate, Juan, Traba Diaz, Juan, Schleicher, Judith, Simon, Judy, Noe, Julia Le, Gerlach, Justin, Eriksson, K. Martin, Prince, Karine, Ostapowicz, Katarzyna, Stajerova, Katerina, Farrell, Katharine N., Snell, Katherine, Yates, Katherine, Fleischer, Katrin, Darras, Kevin, Schumacher, Kim, Orach, Kirill, Thonicke, Kirsten, Riede, Klaus, Heller, Klaus-Gerhard, Wang-Erlandsson, Lan, Pereira, Laura, Riggi, Laura, Florez, Laura V., Emperaire, Laure, Durieux, Laurent, Tatin, Laurent, Rozylowicz, Laurentiu, Latella, Leonardo, Andresen, Louise C., Cahen-Fourot, Louison, de Agua, Luis Borda, Boto, Luis, Lassaletta, Luis, Amo, Luisa, Sekerka, Lukas, Morales, Manuel B., Macia, Manuel J., Suarez, Manuela Gonzalez, Cabeza, Mar, Londo, Marc, Pollet, Marc, Schwieder, Marcel, Peters, Marcell K., D'Amico, Marcello, Casazza, Marco, Florencio, Margarita, Felipe-Lucia, Maria, Gebara, Maria Fernanda, Johansson, Maria, Garcia, Maria Mancilla, Piquer-Rodriguez, Maria, Tengo, Maria, Elias, Marianne, Leve, Marine, Conde, Marta, Winter, Marten, Koster, Martijn, Mayer, Martin, Salek, Martin, Schlerf, Martin, Sullivan, Martin, Baumann, Matthias, Pichler, Melanie, Marselle, Melissa, Oddie, Melissa, Razanajatovo, Mialy, Borregaard, Michael Krabbe, Theurl, Michaela C., Hernandez, Miguel, Krofel, Miha, Kechev, Mihail Ognianov, Clark, Mike, Rands, Mike, Antal, Miklos, Pucetaite, Milda, Islar, Mine, Truong, Minh-Xuan A., Vighi, Morgana, Johanisova, Nadia, Prat, Narcis, Escobar, Neus, Deguines, Nicolas, Rust, Niki, Zafra-Calvo, Noelia, Maurel, Noelie, Wagner, Norman, Fitton, Nuala, Ostermann, Ole, Panferov, Oleg, Ange, Olivia, Canals, Oriol, Englund, Oskar, De Smedt, Pallieter, Petridis, Panos, Heikkurinen, Pasi, Weigelt, Patrick, Henriksson, Patrik J. G., de Castro, Paula Drummond, Matos-Maravi, Pavel, Duran, Paz, Aragon, Pedro, Cardoso, Pedro, Leitao, Pedro J., Hosner, Peter A., Biedermann, Peter, Keil, Petr, Petrik, Petr, Martin, Philip, Bocquillon, Pierre, Renaud, Pierre-Cyril, Addison, Prue, Antwis, Rachael, Carmenta, Rachel, Barrientos, Rafael, Smith, Rebecca, Rocha, Ricardo, Fuchs, Richard, Felix, Rob, Kanka, Robert, Aguilee, Robin, Padro Caminal, Roc, Libbrecht, Romain, Lorrilliere, Romain, van der Ent, Ruud J., Henders, Sabine, Pueyo, Salvador, Roturier, Samuel, Jacobs, Sander, Lavorel, Sandra, Leonhardt, Sara Diana, Fraixedas, Sara, Villen-Perez, Sara, Cornell, Sarah, Redlich, Sarah, De Smedt, Sebastian, van der Linden, Sebastian, Perez-Ortega, Sergio, Petrovan, Silviu, Cesarz, Simone, Sjoberg, Sissel, Caillon, Sophie, Schindler, Stefan, Trogisch, Stefan, Taiti, Stefano, Oppel, Steffen, Lutter, Stephan, Garnett, Tara, Guedes, Thais, Wanger, Thomas Cherico, Kastner, Thomas, Worthington, Thomas, Daw, Tim, Schmoll, Tim, McPhearson, Timon, Engl, Tobias, Rutting, Tobias, Vaclavik, Tomas, Jucker, Tommaso, Robillard, Tony, Krause, Torsten, Ljubomirov, Toshko, Aavik, Tsipe, Richardson, Vanessa A., Masterson, Vanessa Anne, Seufert, Verena, Cathy, Vet Gibault, Colino Rabanal, Victor, Montade, Vincent, Thieu, Vincent, Sober, Virve, Morin, Xavier, Mehrabi, Zia, Gonzalez, Adriana Trompetero, Sanz-Cobena, Alberto, Christie, Alec Philip, Romero-Munoz, Alfredo, Dauriach, Alice, Queiroz, Allan Souza, Golland, Ami, Evans, Amy Louise, Cordero, Ana Maria Araujo, Dara, Andrey, Rilovic, Andro, Pedersen, Anna Frohn, Csergo, Anna Maria, Lewerentz, Anne, Monserand, Antoine, Valdecasas, Antonio G., Doherty, Anya, Semper-Pascual, Asuncion, Bleyhl, Benjamin, Rutschmann, Benjamin, Bongalov, Boris, Hankerson, Brett, Heylen, Brigitte, Alonso-Alvarez, Carlos, Comandulli, Carolina, Frossard, Carolina M., Mckeon, Caroline, Godde, Cecile, Palm, Celinda, Singh, Chandrakant, Sieger, Charlotte Sophie, Ohrling, Christian, Paitan, Claudia Parra, Cooper, Conor, Edler, Daniel, Roessler, Daniela C., Kessner-Beierlein, Daniela, Garcia del Amo, David, Lopez Bosch, David, Gueldner, Dino, Noll, Domink, Motivans, Elena, Canteri, Elisabetta, Garnett, Emma, Malecore, Eva, Brambach, Fabian, Ruedenauer, Fabian, Yin, Fang, Hurtado, Fernando, Mempel, Finn, de Freitas, Flavio Luiz Mazzaro, Pendrill, Florence, Leijten, Floris, Somma, Francesca, Schug, Franz, De Knijf, Geert, Peterson, Gustaf, Pe'er, Guy, Booth, Hollie, Rhee, Howon, Staude, Ingmar, Gherghel, Iulian, Vila Traver, Jaime, Kerner, Janika, Hinton, Jennifer, Hortal, Joaquin, Persson, Joel, Uddling, Johan, Coenen, Johanna, Geldmann, Jonas, Geschke, Jonas, Juergensen, Jonathan, Lobo, Jorge M., Skejo, Josip, Heinen, Julia Helena, Schuenzel, Julia, Daniel-Ferreira, Juliana, Christophe Piquet, Julien, Murtough, Katie L., Prevel, Leonie, Hissa, Leticia B. V., af Segerstad, Louise Hard, Willemse, Luc, Benavides, Lucia, Sovova, Lucie, Figueiredo, Ludmilla, Leidinger, Ludwig, Piemontese, Luigi, da Fonte, Luis Fernando Marin, Moreta, Lys Sanz, Bhan, Manan, Toledo-Hernandez, Manuel, Engert, Manuela, Davoli, Marco, Mas Navarro, Maria, Voigt, Maria, Zirion, Maria, Wandl, Marie-Theres, Kipson, Marina, Johnson, Mark D., Lukic, Marko, Goula, Marta, Jung, Martin, Nunes, Matheus Henrique, Alvarez, Matheus Rodriguez, van den Burg, Matthijs P., Guerrero, Mayra Daniela Pena, Greenfield, Michael, Lobmann, Michael, Nygren, Michelle, Guth, Miriam Karen, Koh, Niak, Stanek, Nicola, Roux, Nicolas, Karagouni, Niki, Tiralla, Nina, Mairota, Paola, Savaget, Paulo, von Doehren, Peer, Benyei, Petra, Lena, Philippe, Rufin, Philippe, Janke, Rebekka, Santagata, Remo, Motta, Renzo, Battiston, Roberto, Oyanedel, Rodrigo, Bernardo-Madrid, Ruben, Vasconcelos, Sasha, Henriques, Sergio, Bager, Simon L., Qin, Siyu, Ivkovic, Slobodan, Cooke, Sophia, Ernst, Stefan, Schmelzer, Stefan, da Silva, Sven, Faberova, Tamara, Enseroth, Tanja, De Marzo, Teresa, Pienkowski, Thomas, Engel, Thore, Boehnert, Tim, Swinfield, Tom, Kurdikova, Vendula, Chvatalova, Veronika, Lopez-Marquez, Violeta, Arlidge, William, and Zhang, Zhijie

Published
2019
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34. Landsat derived land surface phenology metrics for the characterization of natural vegetation in the Brazilian savanna

Author

Schwieder, Marcel, Hostert, Patrick, Fensholt, Rasmus, and Lausch, Angela

Subjects
Land surface phenology, Time series, Landbedeckungskartierung, Cerrado, Carbon estimation, Savanne, Timesat, Remote sensing, Kohlenstoffkartierung, Fernerkundung, 550 Geowissenschaften, RW 60489, Brasilien, Machine learning, Savanna, ddc:550, Phänologie, Maschinelles lernen, Zeitreihe, RB 10232, Landsat, Land cover mapping, Brazil
Abstract

Die Brasilianische Savanne, auch bekannt als der Cerrado, bedeckt ca. 24% der Landoberfläche Brasiliens. Der Cerrado ist von einer einzigartigen Biodiversität und einem starken Gradienten in der Vegetationsstruktur gekennzeichnet. Großflächige Landnutzungsveränderungen haben dazu geführt, dass annähernd die Hälfte der Cerrado in bewirtschaftetes Land umgewandelt wurde. Die Kartierung ökologischer Prozesse ist nützlich, um naturschutzpolitische Entscheidungen auf räumlich explizite Informationen zu stützen, sowie um das Verständnis der Ökosystemdynamik zu verbessern. Neue Erdbeobachtungssensoren, frei verfügbare Daten, sowie Fortschritte in der Datenverarbeitung ermöglichen erstmalig die großflächige Erfassung saisonaler Vegetationsdynamiken mit hohem räumlichen Detail. In dieser Arbeit wird der Mehrwert von Landsat-basierten Landoberflächenphänologischen (LSP) Metriken, für die Charakterisierung der Cerrado-Vegetation, hinsichtlich ihrer strukturellen und phänologischen Diversität, sowie zur Schätzung des oberirdischen Kohlenstoffgehaltes (AGC), analysiert. Die Ergebnisse zeigen, dass LSP-Metriken die saisonale Vegetatiosdynamik erfassen und für die Kartierung von Vegetationsphysiognomien nützlich sind, wobei hier die Grenzen der Einteilung von Vegetationsgradienten in diskrete Klassen erreicht wurden. Basierend auf Ähnlichkeiten in LSP wurden LSP Archetypen definiert, welche die Erfassung und Darstellung der phänologischen Diversität im gesamten Cerrado ermöglichten und somit zur Optimierung aktueller Kartierungskonzepte beitragen können. LSP-Metriken ermöglichten die räumlich explizite Quantifizierung von AGC in drei Untersuchungsgebieten und sollten bei zukünftigen Kohlenstoffschätzungen berücksichtigt werden. Die Erkenntnisse dieser Dissertation zeigen die Vorteile und Nutzungsmöglichkeiten von LSP Metriken im Bereich der Ökosystemüberwachung und haben demnach direkte Implikationen für die Entwicklung und Bewertung nachhaltiger Landnutzungsstrategien. The Brazilian savanna, known as the Cerrado, covers around 24% of Brazil. It is characterized by a unique biodiversity and a strong gradient in vegetation structure. Land-use changes have led to almost half of the Cerrado being converted into cultivated land. The mapping of ecological processes is, therefore, an important prerequisite for supporting nature conservation policies based on spatially explicit information and for deepening our understanding of ecosystem dynamics. New sensors, freely available data, and advances in data processing allow the analysis of large data sets and thus for the first time to capture seasonal vegetation dynamics over large extents with a high spatial detail. This thesis aimed to analyze the benefits of Landsat based land surface phenological (LSP) metrics, for the characterization of Cerrado vegetation, regarding its structural and phenological diversity, and to assess their relation to above ground carbon. The results revealed that LSP metrics enable to capture the seasonal dynamics of photosynthetically active vegetation and are beneficial for the mapping of vegetation physiognomies. However, the results also revealed limitations of hard classification approaches for mapping vegetation gradients in complex ecosystems. Based on similarities in LSP metrics, which were for the first time derived for the whole extent of the Cerrado, LSP archetypes were proposed, which revealed the spatial patterns of LSP diversity at a 30 m spatial resolution and offer potential to enhance current mapping concepts. Further, LSP metrics facilitated the spatially explicit quantification of AGC in three study areas in the central Cerrado and should thus be considered as a valuable variable for future carbon estimations. Overall, the insights highlight that Landsat based LSP metrics are beneficial for ecosystem monitoring approaches, which are crucial to design sustainable land management strategies that maintain key ecosystem functions and services.

Published
2018

35. Mapping woody plant community turnover with space-borne hyperspectral data: a case study in the Cerrado

Author

Leitão, Pedro J., Schwieder, Marcel, Pedroni, Fernando, Sanchez, Maryland, Pinto, José R. R., Maracahipes, Leandro, Bustamante, Mercedes, and Hostert, Patrick

Subjects
Beta diversity, community modelling, GDM, hyperspectral remote sensing, SGDM, species turnover, ddc:57, ddc:55, Veröffentlichung der TU Braunschweig, Publikationsfonds der TU Braunschweig, Article, ddc:5
Abstract

Effective conservation measures require the knowledge on the spatial patterns of species communities and their turnover. This knowledge is, however, many times lacking, particularly so for complex systems. On the other hand, recent developments have resulted in tools that enable the mapping of these patterns from remote sensing data, such as Sparse Generalized Dissimilarity Modelling (SGDM). SGDM is a two-stage approach, which combines a Sparse Canonical Component analysis and a Generalized Dissimilarity Modelling (GDM), thus being designed to deal with high-dimensional data to predict community turnover in GDM. In this study, we use space-borne hyperspectral data to map woody plant community patterns collected in two study sites in the Cerrado (Brazilian savannah), namely, the Parque Estadual da Serra Azul (PESA) in Mato Grosso state and Parque Nacional da Chapada dos Veadeiros (PNCV) in Goiás state. Field data were collected in both study sites, following a systematic sampling scheme adapted for the Cerrado. The Cerrado is the most diverse of all the world's savannahs, and while holding a high diversity and endemism of species, this biome is mostly unprotected and understudied. We used Hyperion data acquired over the two study sites, which were subject to data pre-processing (including radiometric and geometric corrections, as well as correction for sensor errors) and quality screening before analysis. Our models were used to map woody plant community patterns and turnover for the study areas. We also inspected the Hyperion spectral bands which most contributed in the SGDM, for each site. Furthermore, the modelled patterns were interpreted with respect to the ecological characteristics of the respective species, this way further enhancing our understanding of this complex system. This study has demonstrated that this approach is suitable for mapping woody plant communities in heterogeneous systems, based on combined field and space-borne hyperspectral data.

Published
2018

37. From sample to pixel: multi-scale remote sensing data for upscaling aboveground carbon data in heterogeneous landscapes

Author

Leitão, Pedro J., Schwieder, Marcel, Pötzschner, Florian, Pinto, José R. R., Teixeira, Ana M. C., Pedroni, Fernando, Sanchez, Maryland, Rogass, Christian, Linden, Sebastian, Bustamante, Mercedes M. C., and Hostert, Patrick

Subjects
upscaling, aboveground carbon -- Brazilian savanna -- carbon mapping -- Cerrado -- data integration -- ecosystem monitoring -- Hyperion -- hyperspectral -- RapidEye -- upscaling, Brazilian savanna, Cerrado, ecosystem monitoring, aboveground carbon, Article, 550 Geowissenschaften, ddc:57, carbon mapping, hyperspectral, ddc:55, ddc:550, Veröffentlichung der TU Braunschweig, ddc:577, Publikationsfonds der TU Braunschweig, data integration, Hyperion, ddc:5
Abstract

In times of rapid global change, ecosystem monitoring is of utmost importance. Combined field and remote sensing data enable large-scale ecosystem assessments, while maintaining local relevance and accuracy. In heterogeneous landscapes, however, the integration of field-collected data with remote sensing image pixels is not a trivial matter. Indeed, much of the uncertainty in models that use remote sensing to map larger areas lies on the field data integration. In this study, we propose to use fine spatial resolution (5 x 5 m2) remote sensing data as auxiliary data for upscaling field-sampled aboveground carbon data to target (meso-scale, i.e., 30 x 30 m2) image pixels. In this process, we assess the effects of field data disaggregation and extrapolation, with and without the auxiliary data. We test this on three study sites in heterogeneous landscapes of the Brazilian savanna. We thus compare two methods that use auxiliary data—surface method, which uses a weighting layer, and regression method, which applies a regression model—with one method without auxiliary data—cartographic method. To evaluate our results, we compared observed vs. estimated aboveground carbon values (for known samples) at the pixel level. Additionally, we fitted a random forest regression model with the assigned carbon estimates and the target satellite imagery and assessed the influence of the fraction of extrapolated vs. sampled carbon values on model performance. We observed that, in heterogeneous landscapes, the use of fine spatial resolution remote sensing data improves the upscaling of field-based aboveground carbon data to coarser image pixels. We also show that a surface method is more suitable for spatial disaggregation, while a regression approach is preferable for extrapolating non-sampled pixel fractions. In our study, larger datasets, which included a higher proportion of estimated values, generally delivered better models of aboveground carbon than smaller datasets that are assumed to more reliably reflect reality. Our approach enables to link field and remote sensing data, which in turn enables the detailed mapping of aboveground carbon in heterogeneous landscapes over large areas through the optimized integration of field data and multi-scale remote sensing data.

Published
2018

40. From sample to pixel: multi‐scale remote sensing data for upscaling aboveground carbon data in heterogeneous landscapes

Author

Leitão, Pedro J., primary, Schwieder, Marcel, additional, Pötzschner, Florian, additional, Pinto, José R. R., additional, Teixeira, Ana M. C., additional, Pedroni, Fernando, additional, Sanchez, Maryland, additional, Rogass, Christian, additional, van der Linden, Sebastian, additional, Bustamante, Mercedes M. C., additional, and Hostert, Patrick, additional

Published
2018
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43. Mapping woody plant community turnover with space‐borne hyperspectral data – a case study in the Cerrado.

Author

Leitão, Pedro J., Schwieder, Marcel, Pedroni, Fernando, Sanchez, Maryland, Pinto, José R. R., Maracahipes, Leandro, Bustamante, Mercedes, Hostert, Patrick, Nagendra, Harini, and Horning, Ned

Subjects
PLANT communities, REMOTE sensing
Abstract

Effective conservation measures require the knowledge on the spatial patterns of species communities and their turnover. This knowledge is, however, many times lacking, particularly so for complex systems. On the other hand, recent developments have resulted in tools that enable the mapping of these patterns from remote sensing data, such as Sparse Generalized Dissimilarity Modelling (SGDM). SGDM is a two‐stage approach, which combines a Sparse Canonical Component analysis and a Generalized Dissimilarity Modelling (GDM), thus being designed to deal with high‐dimensional data to predict community turnover in GDM. In this study, we use space‐borne hyperspectral data to map woody plant community patterns collected in two study sites in the Cerrado (Brazilian savannah), namely, the Parque Estadual da Serra Azul (PESA) in Mato Grosso state and Parque Nacional da Chapada dos Veadeiros (PNCV) in Goiás state. Field data were collected in both study sites, following a systematic sampling scheme adapted for the Cerrado. The Cerrado is the most diverse of all the world's savannahs, and while holding a high diversity and endemism of species, this biome is mostly unprotected and understudied. We used Hyperion data acquired over the two study sites, which were subject to data pre‐processing (including radiometric and geometric corrections, as well as correction for sensor errors) and quality screening before analysis. Our models were used to map woody plant community patterns and turnover for the study areas. We also inspected the Hyperion spectral bands which most contributed in the SGDM, for each site. Furthermore, the modelled patterns were interpreted with respect to the ecological characteristics of the respective species, this way further enhancing our understanding of this complex system. This study has demonstrated that this approach is suitable for mapping woody plant communities in heterogeneous systems, based on combined field and space‐borne hyperspectral data. Effective conservation measures require the knowledge on the spatial patterns of species communities and their turnover. Recent developments have resulted in tools that enable the mapping of these patterns from remote sensing data, such as Sparse Generalized Dissimilarity Modelling (SGDM). In this paper, we use spaceborne hyperspectral data to map woody plant community patterns in two study sites in the Cerrado, the most diverse of all the world's savannahs, and while holding a high diversity and endemism of species. To this aim, we used Hyperion data, for mapping the woody plant species turnover in our study areas. We also inspected the Hyperion spectral bands which most contributed in the SGDM, for each site. Finally, the modelled patterns were interpreted in respect to the ecological characteristics of the respective species, this way further enhancing our understanding of this complex system [ABSTRACT FROM AUTHOR]

Published
2019
Full Text
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49. Ground-Based Hyperspectral Characterization of Alaska Tundra Vegetation along Environmental Gradients

Author

Buchhorn, Marcel, Walker, Donald A., Heim, Birgit, Raynolds, Martha K., Epstein, Howard E., Schwieder, Marcel, Buchhorn, Marcel, Walker, Donald A., Heim, Birgit, Raynolds, Martha K., Epstein, Howard E., and Schwieder, Marcel

Abstract

Remote sensing has become a valuable tool in monitoring arctic environments. The aim of this paper is ground-based hyperspectral characterization of Low Arctic Alaskan tundra communities along four environmental gradients (regional climate, soil pH, toposequence, and soil moisture) that all vary in ground cover, biomass, and dominating plant communities. Field spectroscopy in connection with vegetation analysis was carried out in summer 2012, along the North American Arctic Transect (NAAT). Spectral metrics were extracted, including the averaged reflectance and absorption-related metrics such as absorption depths and area of continuum removal. The spectral metrics were investigated with respect to “greenness”, biomass, vegetation height, and soil moisture regimes. The results show that the surface reflectances of all sites are similar in shape with a reduced near-infrared (NIR) reflectance that is specific for low-growing biomes. The main spectro-radiometric findings are: (i) Southern sites along the climate gradient have taller shrubs and greater overall vegetation biomass, which leads to higher reflectance in the NIR. (ii) Vegetation height and surface wetness are two antagonists that balance each other out with respect to the NIR reflectance along the toposequence and soil moisture gradients. (iii) Moist acidic tundra (MAT) sites have “greener” species, more leaf biomass, and green-colored moss species that lead to higher pigment absorption compared to moist non-acidic tundra (MNT) sites. (iv) MAT and MNT plant community separation via narrowband Normalized Difference Vegetation Index (NDVI) shows the potential of hyperspectral remote sensing applications in the tundra.

Published
2013

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