• Korean Journal of Agricultural and Forest Meteorology
• /
• v.20 no.1
• /
• pp.101-116
• /
• 2018

Sustainability science is an emerging transdisciplinary research which necessitates not only the communication and collaboration of scientists, practitioners and stakeholders from different disciplines and interests, but also the paradigm shift from deterministic and reductionist approaches to the old basic. Ecological-societal systems (ESS) are co-evolving complex systems having many interacting parts (or agents) whose random interactions at local scale give rise to spontaneous emerging order at global scale (i.e., self-organization). Here, the flows of energy, matter and information between the systems and their surroundings play a key role. We introduce a conceptual framework for such continually morphing dynamical systems, i.e. self-organizing hierarchical open systems (SOHOs). To understand the structure and functionality of SOHOs, we revisit the two fundamental laws of physics. Re-interpretation of these principles helps understand the destiny and better path toward sustainability, and how to reconcile ecosystem integrity with societal vision and value. We then integrate the so-called visioneering (V) framework with that of SOHOs as feedback/feedforward loops so that 'a nudged self-organization' may guide systems' agents to work together toward sustainable ESS. Finally, example is given with newly endorsed Sustainable Development Goals (SDG) Lab (i.e., 'Rural systems visioneering') by Future Earth, which is now underway in rural villages in Tanzania.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.23 no.4
• /
• pp.251-267
• /
• 2021

Remotely sensed vegetation indices (VIs) are empirically related with gross primary productivity (GPP) in various spatio-temporal scales. The uncertainties in GPP-VI relationship increase with temporal resolution. Uncertainty also exists in the eddy covariance (EC)-based estimation of GPP, arising from the partitioning of the measured net ecosystem CO2 exchange (NEE) into GPP and ecosystem respiration (RE). For two forests and two agricultural sites, we correlated the EC-derived GPP in various time scales with three different near-surface remotely sensed VIs: (1) normalized difference vegetation index (NDVI), (2) enhanced vegetation index (EVI), and (3) near infrared reflectance from vegetation (NIRv) along with NIRvP (i.e., NIRv multiplied by photosynthetically active radiation, PAR). Among the compared VIs, NIRvP showed highest correlation with half-hourly and monthly GPP at all sites. The NIRvP was used to test the reliability of GPP derived by two different NEE partitioning methods: (1) original KoFlux methods (GPP_Ori) and (2) machine-learning based method (GPP_ANN). GPP_ANN showed higher correlation with NIRvP at half-hourly time scale, but there was no difference at daily time scale. The NIRvP-GPP correlation was lower under clear sky conditions due to co-limitation of GPP by other environmental conditions such as air temperature, vapor pressure deficit and soil moisture. However, under cloudy conditions when photosynthesis is mainly limited by radiation, the use of NIRvP was more promising to test the credibility of NEE partitioning methods. Despite the necessity of further analyses, the results suggest that NIRvP can be used as the proxy of GPP at high temporal-scale. However, for the VIs-based GPP estimation with high temporal resolution to be meaningful, complex systems-based analysis methods (related to systems thinking and self-organization that goes beyond the empirical VIs-GPP relationship) should be developed.