Remote sensing of soils and vegetation: quantitative parameters retrieval using pattern-recognition techniques and forest stand structure assessment
2011
Kozoderov, V. V. | Dmitriev, E. V.
New findings in numerical mathematics application are outlined in the context of remote-sensing science (RSS), which is an interdisciplinary concept encompassing the proposed modelling approach, multi-spectral (the number of spectral bands is usually not more than 10) and hyper-spectral (hundreds of spectral bands) airspace data processing, validation campaigns of ground-based data use. The modelling approach involves the direct problem of the outgoing radiation calculation and the inverse problem of the retrieval of land surface parameters. Pattern-recognition procedures use a mathematical technique of imagery classification combined with retrieval techniques for each processed pixel. Validation experiments are conducted to compile surface flux data, biogeochemical samples, measurements from specially equipped towers and other parts of data collection for selected plots. The following RSS key areas are considered: the two-dimensional vegetation structure description, instead of the commonly used leaf area index (LAI) improvements; the green phytomass (for leaves/needles) assessment as an alternative to the ‘indices concept’ (Normalized Difference Vegetation Index (NDVI), etc.) using multi-spectral and hyper-spectral imagery processing. Results are shown as the net primary productivity (NPP) assessment for the selected transect of the southern biome of boreal forest in Russia using airborne remote-sensing data, ground-based forest taxation results and validation techniques of pixel-by-pixel classification together with the forest phytomass retrievals that serve to present the relevant results of remote-sensing data processing in terms of the NPP values.
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