ORCID Profile
0000-0002-9627-9565
Current Organisations
University of Zurich
,
Universitat Zurich
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Publisher: Springer Science and Business Media LLC
Date: 23-12-2021
DOI: 10.1038/S41559-021-01616-8
Abstract: Plant functional traits can predict community assembly and ecosystem functioning and are thus widely used in global models of vegetation dynamics and land–climate feedbacks. Still, we lack a global understanding of how land and climate affect plant traits. A previous global analysis of six traits observed two main axes of variation: (1) size variation at the organ and plant level and (2) leaf economics balancing leaf persistence against plant growth potential. The orthogonality of these two axes suggests they are differently influenced by environmental drivers. We find that these axes persist in a global dataset of 17 traits across more than 20,000 species. We find a dominant joint effect of climate and soil on trait variation. Additional independent climate effects are also observed across most traits, whereas independent soil effects are almost exclusively observed for economics traits. Variation in size traits correlates well with a latitudinal gradient related to water or energy limitation. In contrast, variation in economics traits is better explained by interactions of climate with soil fertility. These findings have the potential to improve our understanding of bio ersity patterns and our predictions of climate change impacts on biogeochemical cycles.
Publisher: MDPI AG
Date: 11-10-2019
DOI: 10.3390/RS11202356
Abstract: In the face of rapid global change it is imperative to preserve geo ersity for the overall conservation of bio ersity. Geo ersity is important for understanding complex biogeochemical and physical processes and is directly and indirectly linked to bio ersity on all scales of ecosystem organization. Despite the great importance of geo ersity, there is a lack of suitable monitoring methods. Compared to conventional in-situ techniques, remote sensing (RS) techniques provide a pathway towards cost-effective, increasingly more available, comprehensive, and repeatable, as well as standardized monitoring of continuous geo ersity on the local to global scale. This paper gives an overview of the state-of-the-art approaches for monitoring soil characteristics and soil moisture with unmanned aerial vehicles (UAV) and air- and spaceborne remote sensing techniques. Initially, the definitions for geo ersity along with its five essential characteristics are provided, with an explanation for the latter. Then, the approaches of spectral traits (ST) and spectral trait variations (STV) to record geo ersity using RS are defined. LiDAR (light detection and ranging), thermal and microwave sensors, multispectral, and hyperspectral RS technologies to monitor soil characteristics and soil moisture are also presented. Furthermore, the paper discusses current and future satellite-borne sensors and missions as well as existing data products. Due to the prospects and limitations of the characteristics of different RS sensors, only specific geotraits and geo ersity characteristics can be recorded. The paper provides an overview of those geotraits.
Publisher: Wiley
Date: 25-03-2016
DOI: 10.1002/RSE2.15
Publisher: Elsevier BV
Date: 12-2017
Publisher: MDPI AG
Date: 09-05-2022
DOI: 10.3390/RS14092279
Abstract: Remote sensing (RS) enables a cost-effective, extensive, continuous and standardized monitoring of traits and trait variations of geomorphology and its processes, from the local to the continental scale. To implement and better understand RS techniques and the spectral indicators derived from them in the monitoring of geomorphology, this paper presents a new perspective for the definition and recording of five characteristics of geomorpho ersity with RS, namely: geomorphic genesis ersity, geomorphic trait ersity, geomorphic structural ersity, geomorphic taxonomic ersity, and geomorphic functional ersity. In this respect, geomorphic trait ersity is the cornerstone and is essential for recording the other four characteristics using RS technologies. All five characteristics are discussed in detail in this paper and reinforced with numerous ex les from various RS technologies. Methods for classifying the five characteristics of geomorpho ersity using RS, as well as the constraints of monitoring the ersity of geomorphology using RS, are discussed. RS-aided techniques that can be used for monitoring geomorpho ersity in regimes with changing land-use intensity are presented. Further, new approaches of geomorphic traits that enable the monitoring of geomorpho ersity through the valorisation of RS data from multiple missions are discussed as well as the ecosystem integrity approach. Likewise, the approach of monitoring the five characteristics of geomorpho ersity recording with RS is discussed, as are existing approaches for recording spectral geomorhic traits/ trait variation approach and indicators, along with approaches for assessing geomorpho ersity. It is shown that there is no comparable approach with which to define and record the five characteristics of geomorpho ersity using only RS data in the literature. Finally, the importance of the digitization process and the use of data science for research in the field of geomorphology in the 21st century is elucidated and discussed.
Publisher: Elsevier BV
Date: 09-2013
Publisher: Elsevier BV
Date: 11-2016
Publisher: Wiley
Date: 08-2018
Abstract: Human activities exert stress on and create disturbances to ecosystems, decreasing their ersity, resilience and ultimately the health of ecosystems and their vegetation. In environments with rapid changes in vegetation health (VH), progress is needed when it comes to monitoring these changes and underlying causes. There are different approaches to monitoring VH such as in situ species approaches and the remote‐sensing approach. Here we provide an overview of in situ species approaches, that is, the biological, the phylogenetic, and the morphological species concept, as well as an overview of the remote‐sensing spectral trait/spectral trait variations concept to monitor the status of VH as well as processes of stress, disturbances, and resource limitations affecting VH. The approaches are compared with regard to their suitability for monitoring VH, and their advantages, disadvantages, potential, and requirements for being linked are discussed. No single approach is sufficient to monitor the complexity and multidimensionality of VH over the short to long term and on local to global scales. Rather, every approach has its pros and cons, making it all the more necessary to link approaches. In this paper, we present a framework and list crucial requirements for coupling approaches and integrating additional monitoring elements to form a multisource vegetation health monitoring network (MUSO‐VH‐MN). When it comes to linking the different approaches, data, information, models or platforms in a MUSO‐VH‐MN, big data with its complexity and syntactic and semantic heterogeneity and the lack of standardized approaches and VH protocols pose the greatest challenge. Therefore, Data Science with the elements of (a) digitalization, (b) semantification, (c) ontologization, (d) standardization, (e) Open Science, as well as (f) open and easy analyzing tools for assessing VH are important requirements for monitoring, linking, analyzing, and forecasting complex and multidimensional changes in VH.
Publisher: Elsevier BV
Date: 02-2012
Publisher: Wiley
Date: 03-05-2021
Abstract: Ecosystem heterogeneity has been widely recognized as a key ecological indicator of several ecological functions, ersity patterns and change, metapopulation dynamics, population connectivity or gene flow. In this paper, we present a new R package— raster —to calculate heterogeneity indices based on remotely sensed data. We also provide an ecological application at the landscape scale and demonstrate its power in revealing potentially hidden heterogeneity patterns. The raster package allows calculating multiple indices, robustly rooted in Information Theory, and based on reproducible open‐source algorithms.
Publisher: Springer Science and Business Media LLC
Date: 24-05-2021
Publisher: Cold Spring Harbor Laboratory
Date: 10-02-2021
DOI: 10.1101/2021.02.09.430391
Abstract: Ecosystem heterogeneity has been widely recognized as a key ecological feature, influencing several ecological functions, since it is strictly related to several ecological functions like ersity patterns and change, metapopulation dynamics, population connectivity, or gene flow. In this paper, we present a new R package - raster - to calculate heterogeneity indices based on remotely sensed data. We also provide an ecological application at the landscape scale and demonstrate its power in revealing potentially hidden heterogeneity patterns. The raster package allows calculating multiple indices, robustly rooted in Information Theory, and based on reproducible open source algorithms.
Publisher: SPIE
Date: 08-04-2003
DOI: 10.1117/12.462438
Publisher: Springer Science and Business Media LLC
Date: 22-07-2015
DOI: 10.1038/523403A
Publisher: MDPI AG
Date: 15-07-2018
DOI: 10.3390/RS10071120
Abstract: Forest ecosystems fulfill a whole host of ecosystem functions that are essential for life on our planet. However, an unprecedented level of anthropogenic influences is reducing the resilience and stability of our forest ecosystems as well as their ecosystem functions. The relationships between drivers, stress, and ecosystem functions in forest ecosystems are complex, multi-faceted, and often non-linear, and yet forest managers, decision makers, and politicians need to be able to make rapid decisions that are data-driven and based on short and long-term monitoring information, complex modeling, and analysis approaches. A huge number of long-standing and standardized forest health inventory approaches already exist, and are increasingly integrating remote-sensing based monitoring approaches. Unfortunately, these approaches in monitoring, data storage, analysis, prognosis, and assessment still do not satisfy the future requirements of information and digital knowledge processing of the 21st century. Therefore, this paper discusses and presents in detail five sets of requirements, including their relevance, necessity, and the possible solutions that would be necessary for establishing a feasible multi-source forest health monitoring network for the 21st century. Namely, these requirements are: (1) understanding the effects of multiple stressors on forest health (2) using remote sensing (RS) approaches to monitor forest health (3) coupling different monitoring approaches (4) using data science as a bridge between complex and multidimensional big forest health (FH) data and (5) a future multi-source forest health monitoring network. It became apparent that no existing monitoring approach, technique, model, or platform is sufficient on its own to monitor, model, forecast, or assess forest health and its resilience. In order to advance the development of a multi-source forest health monitoring network, we argue that in order to gain a better understanding of forest health in our complex world, it would be conducive to implement the concepts of data science with the components: (i) digitalization (ii) standardization with metadata management after the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles (iii) Semantic Web (iv) proof, trust, and uncertainties (v) tools for data science analysis and (vi) easy tools for scientists, data managers, and stakeholders for decision-making support.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2014
Publisher: Springer Science and Business Media LLC
Date: 05-2019
Publisher: Oekom Publishers GmbH
Date: 20-10-2022
DOI: 10.14512/GAIA.31.3.3
Abstract: Games as a didactic tool (e. g., puzzles) are gaining recognition in environmental education to promote skill development, but also to develop a specific understanding of the natural world. However, a children’s puzzle containing representations of nature may unwillingly lead to “misconceptions” of bio ersity themes and processes, and an over-simplification of the relationship between people and nature. To solve this problem, positive connotations of bio ersity may prompt a conceptual change to a more nuanced, multifaceted conception of bio ersity.
Publisher: Elsevier BV
Date: 12-2003
Publisher: Elsevier BV
Date: 05-2012
Publisher: IEEE
Date: 2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Elsevier BV
Date: 2008
Publisher: Elsevier BV
Date: 07-2011
Publisher: Springer Science and Business Media LLC
Date: 25-10-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2014
Publisher: Informa UK Limited
Date: 12-2006
Publisher: Elsevier BV
Date: 04-2013
Publisher: MDPI AG
Date: 10-11-2020
DOI: 10.3390/RS12223690
Abstract: The status, changes, and disturbances in geomorphological regimes can be regarded as controlling and regulating factors for bio ersity. Therefore, monitoring geomorphology at local, regional, and global scales is not only necessary to conserve geo ersity, but also to preserve bio ersity, as well as to improve bio ersity conservation and ecosystem management. Numerous remote sensing (RS) approaches and platforms have been used in the past to enable a cost-effective, increasingly freely available, comprehensive, repetitive, standardized, and objective monitoring of geomorphological characteristics and their traits. This contribution provides a state-of-the-art review for the RS-based monitoring of these characteristics and traits, by presenting ex les of aeolian, fluvial, and coastal landforms. Different ex les for monitoring geomorphology as a crucial discipline of geo ersity using RS are provided, discussing the implementation of RS technologies such as LiDAR, RADAR, as well as multi-spectral and hyperspectral sensor technologies. Furthermore, data products and RS technologies that could be used in the future for monitoring geomorphology are introduced. The use of spectral traits (ST) and spectral trait variation (STV) approaches with RS enable the status, changes, and disturbances of geomorphic ersity to be monitored. We focus on the requirements for future geomorphology monitoring specifically aimed at overcoming some key limitations of ecological modeling, namely: the implementation and linking of in-situ, close-range, air- and spaceborne RS technologies, geomorphic traits, and data science approaches as crucial components for a better understanding of the geomorphic impacts on complex ecosystems. This paper aims to impart multidimensional geomorphic information obtained by RS for improved utilization in bio ersity monitoring.
Publisher: Springer Science and Business Media LLC
Date: 19-07-2021
Publisher: Springer Science and Business Media LLC
Date: 13-05-2021
DOI: 10.1038/S41559-021-01451-X
Abstract: Monitoring global bio ersity from space through remotely sensing geospatial patterns has high potential to add to our knowledge acquired by field observation. Although a framework of essential bio ersity variables (EBVs) is emerging for monitoring bio ersity, its poor alignment with remote sensing products hinders interpolation between field observations. This study compiles a comprehensive, prioritized list of remote sensing bio ersity products that can further improve the monitoring of geospatial bio ersity patterns, enhancing the EBV framework and its applicability. The ecosystem structure and ecosystem function EBV classes, which capture the biological effects of disturbance as well as habitat structure, are shown by an expert review process to be the most relevant, feasible, accurate and mature for direct monitoring of bio ersity from satellites. Bio ersity products that require satellite remote sensing of a finer resolution that is still under development are given lower priority (for ex le, for the EBV class species traits). Some EBVs are not directly measurable by remote sensing from space, specifically the EBV class genetic composition. Linking remote sensing products to EBVs will accelerate product generation, improving reporting on the state of bio ersity from local to global scales.
Publisher: Springer Science and Business Media LLC
Date: 15-02-2019
Publisher: The Royal Society
Date: 07-2014
Abstract: Remote sensing using airborne imaging spectroscopy (AIS) is known to retrieve fundamental optical properties of ecosystems. However, the value of these properties for predicting plant species distribution remains unclear. Here, we assess whether such data can add value to topographic variables for predicting plant distributions in French and Swiss alpine grasslands. We fitted statistical models with high spectral and spatial resolution reflectance data and tested four optical indices sensitive to leaf chlorophyll content, leaf water content and leaf area index. We found moderate added-value of AIS data for predicting alpine plant species distribution. Contrary to expectations, differences between species distribution models (SDMs) were not linked to their local abundance or phylogenetic/functional similarity. Moreover, spectral signatures of species were found to be partly site-specific. We discuss current limits of AIS-based SDMs, highlighting issues of scale and informational content of AIS data.
Publisher: Elsevier BV
Date: 15-03-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2015
Publisher: IEEE
Date: 2007
No related grants have been discovered for Michael Schaepman.