Our team consists of about fifteen researchers, including one full-time professor, one part-time professor, 2 post-doctoral researchers, and approximately ten Ph.D. researchers. We conduct research for the European Union (EU) and for Belgian and Flemish governmental agencies like the Flemish Fund for Scientific Research (FWO), the Institute for the Promotion of Innovation by Science and Technology in Flanders (IWT), and the Belgian Federal Science Policy Office (BELSPO). The results of our research are customarily published via Ph.D. theses, in international peer-reviewed journals, and in periodic reports to funding agencies.

102 Vital Decoster Street
B-3000 Leuven

The aim of our research group is to develop new methods to extract spatial information of forests and other ecosystems that may contribute to a better understanding and a sustainable management of these systems. These methods have specific bearing on quantitative modeling of (i) the interaction between electro-magnetic energy and living vegetative systems and (ii) the water-, carbon-, and nitrogen cycles in agricultural and forest ecosystems. Approaches are generally based on recent technologies of quantitative analysis, integrating earth observation, 3-D modeling (computer graphics), process-modeling, geodesy, geographical information systems (GIS) and databases, and statistical/probability analyses.

In addition, we provide training in geomatics (earth observation, GIS, surveying, and geodesy), natural resource inventory (dendrometry, inventory, and population statistics), and in the monitoring/modeling of vegetative production systems. Training takes place within the framework of a bachelors degree in the bio-engineering sciences (land- and forest management option), a M.Sc. degree in bio-engineering sciences (land- and forest management), and a continued M.Sc. degree in Earth Observation in cooperation with Purdue University, USA.

Earth observation using terrestrial sensors (laser/lidar sensing. hemispheric photography, etc.) and space- or airborne platforms produces images that allow us to investigate human land use, its changes over time in a specific region, and vegetative process modeling at various scales. The resolution of remote sensing images depends directly on the distance between object and platform, which varies in the range of meters (hand-held instruments) to 800 kilometers or more (satellites). Images may be of analog (conventional photography) or digital (laser measurements) types. We specifically use remotely sensed data to investigate and model dynamic vegetative processes, e.g., photosynthesis, evapotranspiration, biomass accumulation, CO2 balances, ecosystem vitality, production potential, and fire risk, among others. Results potentially also could be incorporated in maps as part of decision support systems focused on production and resource management.

Satellite images with high spatial resolution allow for the detection of small-scale changes in vegetative systems, e.g., the detection of stress in individual fruit trees or groups of forest trees. The integration of hyperspectral sensors (with high spectral resolution) and digital terrestrial instruments (designed and built to purpose) enables us, for example, to evaluate forest vitality or to predict fruit quality and harvest in orchards and vineyards. Real-time monitoring furthermore allows us to manage vegetative processes based on information obtained from such an integrated system. We can use imagery with a high temporal resolution (time series analysis) to study generally large-scale process dynamics. Examples include the monitoring of vegetative regrowth after natural disasters (fire, flooding, etc.), evaluation of vegetative fire sensitivity (through water and environmental indicators), and the development of fire risk indicators.

Although remote sensing technology allows us to detect distinct imagery changes among collection dates, it often remains unclear which ground phenomena are the causal agents. Parallel quantitative ground measurements are therefore essential and we focus on quantities like leaf area, vitality, and structural forest indicators. We are in the process of developing a complex virtual 3D-forest model, in order to improve our understanding of the principal relationships between remote sensing and ground-based measurements in a forest environment. Such a model will enable us to simulate the 3D reflection of a forest canopy, based on interactions between the elements of the forest canopy and incoming light energy. We also are creating a fully automated ground-based LIDAR measurement system that will facilitate data acquisition for 3D-models and allow the direct derivation of forest structural variables. The virtual forest model incorporates both the photon-vegetation interaction and geometric structure of the forest canopy. We are attempting to develop a standardized structural descriptor that will be exclusive, as well as repeatable.

We also are attempting to contribute to the knowledge and management of tropical natural resources. For example, we are studying the effect of the ENSO cycle (El Nino – La Nina) on Miombo dry-forests of southern Africa.

Finally, we are focusing on the integration of newly acquired remote sensing knowledge with other information sources to improve the understanding of and provide concrete support for the management of natural resources.

Close collaboration with other research groups, in terms of both research and education, is necessary in this field of fast developing high-technology. Intensive collaborative efforts have been set up with famous international partners, including:

• Laboratory for Applications of Remote Sensing (LARS), Purdue University, USA.
• National Centre for Landscape Fire Analysis (NCLFA), University of Montana, USA.
• Remote Sensing Laboratory (RSL), University of Minnesota, USA,
• SunSpace and Engineering Faculty, University of Stellenbosch, South Africa.
• Centre de Recherches en Géomatique (CRG), Université Laval, Quebec, Canada.
• Institute of Environment and Sustainability, Joint Research Centre, European Commission, Ispra, Italy
• Université Catholique de Louvain, Département de Géographie, Louvain-La-Neuve, Belgium
• Faculté Universitaire des Sciences Agronomiques de Gembloux, Unité de Biométrie, Gembloux, Belgium