Abstract 1. Human activities exert stress on and create disturbances to ecosystems, decreas- ing their diversity, resilience and ultimately the health of ecosystems and their vegetation. In environments with rapid changes in vegetation health (VH), pro- gress is needed when it comes to monitoring these changes and underlying causes. There are different approaches to monitoring VH such as in situ species ap- proaches and the remote-sensing approach. 2. 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 limita- tions affecting VH. The approaches are compared with regard to their suitability for monitoring VH, and their advantages, disadvantages, potential, and require- ments for being linked are discussed. 3. No single approach is sufficient to monitor the complexity and multidimensional- ity 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). 4. When it comes to linking the different approaches, data, information, models or plat- forms 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 moni- toring, linking, analyzing, and forecasting complex and multidimensional changes in VH.
Lausch, A., Bastian, O., Klotz, S., Leitão, P.J., Jung, A., Rocchini, D., et al. (2018). Understanding and assessing vegetation health by in situ species and remote-sensing approaches. METHODS IN ECOLOGY AND EVOLUTION, 9(8), 1799-1809 [10.1111/2041-210X.13025].
Understanding and assessing vegetation health by in situ species and remote-sensing approaches
Rocchini, Duccio;
2018
Abstract
Abstract 1. Human activities exert stress on and create disturbances to ecosystems, decreas- ing their diversity, resilience and ultimately the health of ecosystems and their vegetation. In environments with rapid changes in vegetation health (VH), pro- gress is needed when it comes to monitoring these changes and underlying causes. There are different approaches to monitoring VH such as in situ species ap- proaches and the remote-sensing approach. 2. 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 limita- tions affecting VH. The approaches are compared with regard to their suitability for monitoring VH, and their advantages, disadvantages, potential, and require- ments for being linked are discussed. 3. No single approach is sufficient to monitor the complexity and multidimensional- ity 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). 4. When it comes to linking the different approaches, data, information, models or plat- forms 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 moni- toring, linking, analyzing, and forecasting complex and multidimensional changes in VH.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
