3D modelling of branching in plants

Shoot branching is a key determinant of overall aboveground plant form. During plant development, the number of branches formed strongly influences the amount of light absorbed by the plant, and thus the plant’s competitive strength in terms of light capture in relation to neighbouring plants. Branching is regulated by multiple internal factors which are modulated by different environmental signals. A key environmental signal in the context of a plant population is a low red / far-red intensity ratio (R:FR) of the light reflected by neighbouring plants. For instance, low R:FR results in suppression of branching in favour of elongation growth, which is a key aspect of shade avoidance. Shade avoidance enables plants to anticipate future competition by preventing being shaded, rather than to react to prevailing shade conditions. Internally, branching is regulated by a finely tuned plant hormone network. The interactions within this network are modified by environmental cues such as R:FR which is perceived by specific photoreceptors. Combined, internal and external signals enable regulation of branch formation under the influence of environmental conditions. The different aspects of branching control act at different levels of biological organization (organ, whole plant, plant community). These aspects can be integrated in one modelling approach, called functional-structural plant modelling (FSPM), explicitly considering spatial 3D plant development. An FSP model typically contains detailed information at any moment in development of the plant on the number, size, location and orientation of all organs that make up the plant. In FSP models, physiological and physical processes occur within the plant (e.g. photosynthesis and transport of assimilates), and interaction with the environment occurs at the interface of organ and environment (e.g. light absorption by a leaf). Explicit simulation of absorption and scattering of light at the level of the plant organ is an important aspect of FSPM. In combination with dedicated experiments, this modelling tool can be used to analyse the response of plants to (imminent) competition, simulate the competitive advantage of shade avoidance for plants of different architecture, and predict plant form in various light environments. To assess the effect of plant population density through R:FR signalling on tillering (branching) in spring wheat (Triticum aestivum L.), an FSPM study was conducted (Figure 1). A simple descriptive relationship was used to link R:FR as perceived by the plant to extension growth of tiller buds and probability of a bud to form a tiller. A further study included a complete sub-model of branching regulation, aiming at simulating branching as an emergent property in Arabidopsis (Arabidopsis thaliana) under the influence of R:FR. These and other studies show that FSPM is a promising tool to simulate aspects of plant development, such as branching, under the influence of environmental factors. In close combination with dedicated experiments, FSPM can shape our ideas of the mechanisms controlling plant development, can integrate existing knowledge on plant development, and can predict plant development in untested conditions

A review on integration of artificial intelligence into water quality modelling

2005-2006 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Intelligent manipulation of calibration parameters in numerical modeling

Author name used in this publication: K. W. Chau2003-2004 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Effects of flow regime on the young stages of Salmonid fishes. Conclusions based on results for 1977-1981

Rivers in Teesdale and its fish population have been monitored for several years. This report briefly describes the life cycle of British salmonid fishes and indicates the main ways in which this life cycle is influenced by discharge and related effects. Some highlights of the research results for 1977 - 1981 are briefly stated and proposals for future research are listed. Some practical implications of the results are discussed. (PDF contains 34 pages

Using Data in Undergraduate Science Classrooms

Provides pedagogical insight concerning the skill of using data The resource being annotated is: http://www.dlese.org/dds/catalog_DATA-CLASS-000-000-000-007.htm

Intelligent manipulation and calibration of parameters for hydrological models

Author name used in this publication: K. W. Chau2006-2007 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Manipulation of numerical coastal flow and water quality models

Author name used in this publication: K. W. Chau2002-2003 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Airborne LiDAR for DEM generation: some critical issues

Airborne LiDAR is one of the most effective and reliable means of terrain data collection. Using LiDAR data for DEM generation is becoming a standard practice in spatial related areas. However, the effective processing of the raw LiDAR data and the generation of an efficient and high-quality DEM remain big challenges. This paper reviews the recent advances of airborne LiDAR systems and the use of LiDAR data for DEM generation, with special focus on LiDAR data filters, interpolation methods, DEM resolution, and LiDAR data reduction. Separating LiDAR points into ground and non-ground is the most critical and difficult step for DEM generation from LiDAR data. Commonly used and most recently developed LiDAR filtering methods are presented. Interpolation methods and choices of suitable interpolator and DEM resolution for LiDAR DEM generation are discussed in detail. In order to reduce the data redundancy and increase the efficiency in terms of storage and manipulation, LiDAR data reduction is required in the process of DEM generation. Feature specific elements such as breaklines contribute significantly to DEM quality. Therefore, data reduction should be conducted in such a way that critical elements are kept while less important elements are removed. Given the highdensity characteristic of LiDAR data, breaklines can be directly extracted from LiDAR data. Extraction of breaklines and integration of the breaklines into DEM generation are presented

Development of an integrated knowledge-based system on flow and water quality in Hong Kong coastal waters

Author name used in this publication: K. W. Chau2006-2007 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

A review on the integration of artificial intelligence into coastal modeling

Author name used in this publication: Kwokwing Chau2005-2006 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe