Polygon-based aggregation of remotely sensed data for regional ecological analyses

Wicks, T.E., Smith, G.M. and Curran, P.J. (2002) Polygon-based aggregation of remotely sensed data for regional ecological analyses. International Journal of Applied Earth Observation and Geoinformation, 4 (2), 161-173. (doi:10.1016/S0303-2434(02)00025-9).

Abstract

Given current concerns about global climate change, there is an urgent need to quantify and monitor accurately the magnitude of present day terrestrial carbon sinks. This may be achieved by driving ecosystem simulation models (ESMs) spatially with remotely sensed estimates of ecological variables, such as leaf area index (LAI). Conventional procedures for analysing digital remotely sensed images rely upon pixel-based methods, using spectral information from each pixel to allocate it to a land cover type or estimate a surface property (e.g. LAI). Groups of pixels, within areas assumed to be ‘thematically homogeneous’, will not necessarily provide the same allocation or estimation due to data noise, atmospheric effects and natural variation of the surface. Pixels on the boundary between areas are an additional problem as their spectral information derives from more than one surface type. If contextual information on the spatial pattern and structure of the landscape could be included in the analysis (e.g. forest inventory polygons, agricultural land parcels), then the accuracy of the allocations or estimations (e.g. of LAI) could be increased. Polygon-based approaches, where all pixels within a defined area are presumed similar and so can be combined prior to analysis, offer a solution. These approaches are implemented most efficiently within an integrated GIS where raster and ancillary data can be analysed with reference to vector land polygons. A procedure using remotely sensed data in a polygon format to produce accurate spatial estimates of LAI (on which to drive an ESM) is described. In relation to a pixel-based procedure, the polygon-based procedure provided: (1) increased accuracy, (2) more appropriate and realistic representations of the environment and (3) a powerful and flexible framework for further data analysis.

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