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Modelling Net Primary Productivity and Above-Ground Biomass for Mapping of Spatial Biomass Distribution in Kazakhstan

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Modelling Net Primary Productivity and Above-Ground Biomass for Mapping of Spatial Biomass Distribution in Kazakhstan Faculty of Environmental Sciences Modelling Net Primary Productivity and Above-Ground Biomass for Mapping of Spatial Biomass Distribution in Kazakhstan Dissertation for awarding the academic degree Doktoringenieur (Dr.-Ing.) Submitted by Dipl.-Ing. Christina Eisfelder Supervisor: Prof. Dr. phil. habil. Manfred F. Buchroithner Technische Universität Dresden, Institute for Cartography Prof. Dr. rer. nat. habil. Stefan Dech German Aerospace Center, German Remote Sensing Data Center Prof. Dr. Martin Kappas Georg-August-Universität Göttingen, Institute of Geography Date of submission: 20 December 2012 Dresden, 20 June 2013 Erklärung des Promovenden Die Übereinstimmung dieses Exemplars mit dem Original der Dissertation zum Thema: “Modelling Net Primary Productivity and Above-Ground Biomass for Mapping of Spatial Biomass Distribution in Kazakhstan” wird hiermit bestätigt. …………………………………… Ort, Datum …………………………………… Unterschrift This dissertation was prepared at the German Remote Sensing Data Center (DFD) of the German Aerospace Center (DLR), Oberpfaffenhofen Acknowledgements Acknowledgements The completion of this dissertation would not have been possible without the valuable support from various people and institutions to whom I would like to express my sincere gratitude. First of all, I would like to thank my supervisor Prof. Dr. Manfred Buchroithner from Dresden University of Technology for his supervision and interest in this work. He always supported this thesis with helpful advice. I am also grateful to Prof. Dr. Stefan Dech, director of the German Remote Sensing Data Center (DFD) of the German Aerospace Center (DLR) for his role as co-reviewer and for giving me the opportunity to pursue this study under the Network EOS (Integrated Earth Observation System) PhD Program at DLR-DFD. It has been a great pleasure for me to work at his institute at the German Aerospace Center, Oberpfaffenhofen. Many thanks also to Prof. Dr. Martin Kappas for taking over the third review. I would especially like to express my sincere thank to Dr. Claudia Künzer, team leader of the team ‘Land Surface Dynamics’ at DFD and supervisor of this thesis at DLR, for her constant support, advice, and constructive discussions. Her steady interest in this work and encouragement gave me strength to stay the course. I am also grateful to Andreas Müller, head of the ‘Land Surface’ department, for facilitating the accomplishment of this work in his department, and to my colleagues from the team ‘Land Surface Dynamics’ for the always friendly, helpful, and motivating atmosphere. Particularly, I would like to sincerely thank Igor Klein for his invaluable support during my field campaign to Kazakhstan in June 2011. Without his translating and assistance the extensive field campaign would not have been possible. Many thanks also for translating the abstract to Russian. Moreover, I would like to thank my colleagues Markus Niklaus and Markus Tum for their cooperation and help regarding processing with BETHY/DLR and for commenting on various parts of this thesis. Thanks also to Ursula Geßner for advice with error propagation. I am also grateful to several people from Kazakhstan, who helped me during my field campaign. First of all, very warm thanks to Prof. Dr. Farida Akiyanova from the Institute of Geography of the Academy of Sciences, Almaty, for her friendly advice and help with finding field assistants and providing equipment for the field campaign. Thanks also for providing the vegetation map from the National Atlas of the Republic of Kazakhstan and permission to use the map within this thesis. I would especially like to thank my field assistants Aruzhan Bekkulieva and Zhandos Kanafin from the Institute of Geography, as well as Arman Ashirbaev for always keeping up an enjoyable atmosphere despite the hard work and long working hours during the field campaign. Moreover, I am very grateful to Dr. Kazbek Toleubayev from the Kazakh Research Institute for Plant Protection and Quarantine, Almaty, and his laboratory staff for oven drying of my biomass samples from both field campaigns in 2010 and 2011. I am also grateful to Thomas Eiglsperger and Alexander Kotenkov for collecting biomass samples in Central Kazakhstan during a field campaign to Kazakhstan carried out on behalf of DLR in December 2010. Many thanks also to Prof. Dr. Martin Kappas and Dr. Pavel Propastin from University of Göttingen for offering me the opportunity to join their field trip to Kazakhstan in June 2010. i Acknowledgements I would like to extend my gratitude to Dr. Miriam Machwitz for advice with pre-processing of input data for the RBM and model application, as well as to Dr. Jochen Richters for advice regarding the RBM and interpretation of results from the model comparison. Many thanks go to Pierre Hiernaux from the Centre d'Etudes Spatiales de la Biosphère, Toulouse, for his advice with in situ measurements. I would also like to thank Dr. Niels Thevs and Allan Buras from the working group ‘Landscape Ecology and Ecosystem Dynamics’ of the Ernst-Moritz-Arndt-Universität Greifswald for general advice with field work in Central Asia, literature recommendations, and providing allometric equations. Thanks also to Ulrike Dittmann for help with digitising of field protocols, Moritz Mössner for assistance with downloading and pre-processing of MODIS data and digitising of field protocols, and Thomas Baldauf for assistance with downloading and preparation of climate station data. Furthermore, I would like to thank Thomas Hahmann, Dr. Danielle Hoja, and Stefan Hahmann for proofreading and commenting on various parts of this thesis, as well as Dr. Beatrix Weber and Steve and Lesley Hoad for proofreading of English language. Very special thanks deserves my boyfriend Thomas Hahmann for his constant support, endless patience, and encouragement. Your love and understanding helped me through the challenging times during the preparation of this thesis. Finally, I would like to express my gratitude to my parents for their support during my studies that led me here. ii Abstract Abstract Biomass is an important ecological variable for understanding the responses of vegetation to the climate system and currently observed global change. In semi-arid areas it especially influences environmental processes, such as the hydrological cycle, soil erosion and degradation. It is therefore essential to develop accurate and transferable methods for biomass estimation in these areas. The quantification of biomass for large areas and long time periods is necessary to identify and monitor those areas under high risk of degradation and desertification. This can only be achieved using data collected from satellite remote sensing. The impact of changes in vegetation biomass on the global ecosystem is of high relevance and may have a critical influence on the future evolution of the climate. Modelling net primary productivity (NPP) is an important instrument for analysing carbon exchange between atmosphere and vegetation. It allows for quantification of carbon sinks and sources. The analysis of NPP time-series and their spatio-temporal patterns helps us to understand ecological functioning and potential disturbances. The vegetation in the arid and semi-arid environments of Kazakhstan faces extreme climatic conditions. Land degradation and desertification already pose large ecological challenges. The region is expected to be affected particularly strongly by future climate change. In Central Asia further rises in temperature and an intensification of aridity are predicted. In the context of diverse anthropogenic and climatic influences on the Kazakh environment, it is of great interest to observe large-scale vegetation dynamics and biomass distribution. In this dissertation, previous research activities and remote-sensing-based methods for biomass estimation in semi-arid regions have been comprehensively reviewed for the first time. The review revealed that the biggest challenge is the transferability of methods in time and space. Empirical approaches, which are predominantly applied, proved to be hardly transferable. This lack of transferability hinders a repeated or operational application of biomass estimation methods. Remote-sensing-based NPP models, on the other hand, allow for regional to continental modelling of NPP time-series and are potentially transferable to new regions. This thesis thus deals with modelling and analysis of NPP time-series for Kazakhstan and presents a methodological concept for derivation of above-ground biomass (AGB) estimates based on NPP data. The focus for the methodological development was on biomass estimation for natural and semi-natural environments, which cover about 70% of the area of Kazakhstan. For validation of the results, biomass field data were collected in June 2011 in three study areas in Central, South, and West Kazakhstan. Additionally, field data was also collected in Central Kazakhstan in December 2010. For the selection of an appropriate model, two remote-sensing-based NPP models were applied to a study area in Central Kazakhstan. The first is the Regional Biomass Model (RBM), a light-use-efficiency model. The second is the Biosphere Energy Transfer Hydrology Model adapted by DLR (BETHY/DLR), which is a soil-vegetation-atmosphere-transfer model. Both models calculate the NPP on a regional scale of approximately 1 km² spatial resolution and were applied to Kazakhstan for the first time in this dissertation. Differences in the modelling approaches, intermediate products, and calculated NPP, as well as their temporal characteristics were analysed and discussed. The model BETHY/DLR calculated higher NPP (mean annual NPP 2010 and 2011: 136.9 g C m-2 and 106.7 g C m-2) than the RBM (62.1 g C m-2 and 54.6 g C m-2) and showed stronger inter-annual changes. The comparison to field data from 2011 yielded better results for BETHY/DLR, though the results iii Abstract from both models were highly correlated to the field observations (BETHY/DLR: R=0.97, RMSE=8.4 g C m-2; RBM: R=0.99, RMSE=22.5 g C m-2). The model BETHY/DLR was then applied with a regional land cover map to calculate NPP for Kazakhstan for 2003–2011.
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