@article{LauschBorgBumbergeretal.2018,
  author    = {Lausch, Angela and Borg, Erik and Bumberger, Jan and Dietrich, Peter and Heurich, Marco and Huth, Andreas and Jung, Andr{\´a}s and Klenke, Reinhard and Knapp, Sonja and Mollenhauer, Hannes and Paasche, Hendrik and Paulheim, Heiko and Pause, Marion and Schweitzer, Christian and Schmulius, Christiane and Settele, Josef and Skidmore, Andrew K. and Wegmann, Martin and Zacharias, Steffen and Kirsten, Toralf and Schaepman, Michael E.},
  title     = {Understanding forest health with remote sensing, part III: requirements for a scalable multi-source forest health monitoring network based on data science approaches},
  series = {Remote Sensing},
  volume    = {10},
  journal   = {Remote Sensing},
  number    = {7},
  issn      = {2072-4292},
  doi       = {10.3390/rs10071120},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-197691},
  pages     = {1120},
  year      = {2018},
  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.},
  language  = {en}
}
@article{LatifiHeurich2019,
  author    = {Latifi, Hooman and Heurich, Marco},
  title     = {Multi-scale remote sensing-assisted forest inventory: a glimpse of the state-of-the-art and future prospects},
  series = {Remote Sensing},
  volume    = {11},
  journal   = {Remote Sensing},
  number    = {11},
  issn      = {2072-4292},
  doi       = {10.3390/rs11111260},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-197358},
  year      = {2019},
  abstract  = {Advances in remote inventory and analysis of forest resources during the last decade have reached a level to be now considered as a crucial complement, if not a surrogate, to the long-existing field-based methods. This is mostly reflected in not only the use of multiple-band new active and passive remote sensing data for forest inventory, but also in the methodic and algorithmic developments and/or adoptions that aim at maximizing the predictive or calibration performances, thereby minimizing both random and systematic errors, in particular for multi-scale spatial domains. With this in mind, this editorial note wraps up the recently-published Remote Sensing special issue "Remote Sensing-Based Forest Inventories from Landscape to Global Scale", which hosted a set of state-of-the-art experiments on remotely sensed inventory of forest resources conducted by a number of prominent researchers worldwide.},
  language  = {en}
}
@article{LatifiHolzwarthSkidmoreetal.2021,
  author    = {Latifi, Hooman and Holzwarth, Stefanie and Skidmore, Andrew and Brůna, Josef and Červenka, Jaroslav and Darvishzadeh, Roshanak and Hais, Martin and Heiden, Uta and Homolov{\´a}, Lucie and Krzystek, Peter and Schneider, Thomas and Star{\´y}, Martin and Wang, Tiejun and M{\"u}ller, J{\"o}rg and Heurich, Marco},
  title     = {A laboratory for conceiving Essential Biodiversity Variables (EBVs)—The 'Data pool initiative for the Bohemian Forest Ecosystem'},
  series = {Methods in Ecology and Evolution},
  volume    = {12},
  journal   = {Methods in Ecology and Evolution},
  number    = {11},
  doi       = {10.1111/2041-210X.13695},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-262743},
  pages     = {2073-2083},
  year      = {2021},
  abstract  = {Effects of climate change-induced events on forest ecosystem dynamics of composition, function and structure call for increased long-term, interdisciplinary and integrated research on biodiversity indicators, in particular within strictly protected areas with extensive non-intervention zones. The long-established concept of forest supersites generally relies on long-term funds from national agencies and goes beyond the logistic and financial capabilities of state- or region-wide protected area administrations, universities and research institutes. We introduce the concept of data pools as a smaller-scale, user-driven and reasonable alternative to co-develop remote sensing and forest ecosystem science to validated products, biodiversity indicators and management plans. We demonstrate this concept with the Bohemian Forest Ecosystem Data Pool, which has been established as an interdisciplinary, international data pool within the strictly protected Bavarian Forest and Šumava National Parks and currently comprises 10 active partners. We demonstrate how the structure and impact of the data pool differs from comparable cases. We assessed the international influence and visibility of the data pool with the help of a systematic literature search and a brief analysis of the results. Results primarily suggest an increase in the impact and visibility of published material during the life span of the data pool, with highest visibilities achieved by research conducted on leaf traits, vegetation phenology and 3D-based forest inventory. We conclude that the data pool results in an efficient contribution to the concept of global biodiversity observatory by evolving towards a training platform, functioning as a pool of data and algorithms, directly communicating with management for implementation and providing test fields for feasibility studies on earth observation missions.},
  language  = {en}
}