Mr. Chhung-Huor Ung Profile

Chhung-Huor Ung

Research Scientist at Natural Resources Canada

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Proceedings Article | 20 October 2005 Paper

Estimating forest biomass using scale linkage from tree to Landsat-TM reflectance data

Chhun-Huor Ung, Marie-Claude Lambert, Frédéric Raulier

Proceedings Volume 5976, 59761C (2005) https://doi.org/10.1117/12.626416

KEYWORDS: Biological research, Reflectivity, Error analysis, Landsat, Carbon, Data modeling, Statistical analysis, Vegetation, Earth observing sensors, Databases

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Estimates of forest biomass are needed to account for carbon at the tree, stand and regional scales. Sample plots of national forest inventories provide the basic database for these estimates. At the tree scale, a common estimation method is the use of an allometric equation that relates a tree's predicted compartment biomass y_i (i = foliage, branches, stem wood or stem bark) with easily obtained non-destructive measurements, i.e., diameter at breast height (D): y_i=b_i1D^bi2 or with both D and tree height (H): y_i=b_i1D^bi2H^bi3, b_ik being the parameters estimated. A common paradigm observed in biomass literature considers that parameter values vary between stands and regions. At the regional scale, however, when comparing national biomass equations to regional biomass equations, our results showed no significant differences between both types of equation. These results contribute to strengthening the allometric theory as an organizing principle for quantifying the relationship between tree size and biomass across spatial scales. In tandem with the allometry theory, we used a soil-canopy model based on Li-Strahler's approach for up-scaling biomass from the tree to stand scale in a mixed hardwood-coniferous forest. Our results indicated that the shadow fraction of Landsat TM reflectance was correlated with stand biomass. However, this model is indebted with heteroscedasticity, meaning that its error increases appreciably when stand biomass density is high.

Proceedings Article | 30 December 1997 Paper

Forest productivity assessment using remote sensing and GIS-based tools: test case for eastern Canada

Richard Fournier, C. Ung, P. Bernier

Proceedings Volume 3222, (1997) https://doi.org/10.1117/12.298131

KEYWORDS: Remote sensing, Ecosystems, Climatology, Modeling, Geographic information systems, Data modeling, Forestry, Solar radiation models, Environmental sensing, Satellites

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Monitoring net primary productivity (NPP) and assessing site potential at the landscape level are central issues for sustainable landscape management practice. One of the foundations of classical forestry is the computation of forest productivity at the stand level. This computation of growth potential is usually based on a site index derived from measurements of height and age taken on dominant trees. The site index thus integrates the effect of the three main components of forest productivity: vegetation characteristics, climatic environment and site properties. However useful, site index-based methods cannot discern which part of the overall productivity is due to any of these three components. With an increasing demand for ensuring the sustainability of our forest practices, and with the uncertainty engendered by the possibility of climatic change, we now need tools to quantify the intrinsic productivity of a site, and the effect of external factors on this productivity. The ECOLEAP (extended concentration to link ecophysiology and forest productivity) project address these concerns. The overall objective of the ECOLEAP project is to develop a knowledge base from which tools to predict and monitor forest productivity and health will be developed. More specifically, the project will focus on three specific aspects. The first is the measurement of net primary productivity (NPP) in major eastern forest ecosystems, and, within each ecosystem, across a climate or fertility gradient. The second is the determination of functional relationships between specific biophysical factors, related to site, climate and species composition, and ecosystem NPP. The third is to develop tools to predict and monitor the sustainable potential NPP of major forest ecosystems under natural and managed scenarios. The three major forest types under study in ECOLEAP are the sugar maple, balsam fir and black spruce forests.

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